As a Member of Technical Staff (MTS - Staff Engineer) within the company, you will own the architecture, reliability, and evolution of Collinear’s AI agent improvement product offerings. This role is deeply hands-on and focused on backend systems, infrastructure, and distributed execution, with a strong emphasis on performance, scalability, and enterprise readiness.
You will be responsible for designing and operating the systems that support multiple customers, multiple deployment models, and an expanding set of evaluation and simulation scenarios. While people management and hiring are nice to have, the priority is technical leadership, architectural judgment, and execution in a fast-moving, customer-facing environment.
This role is ideal for someone who has built and operated cloud services at scale and wants to apply that experience to a complex, high-impact AI platform.
Own the architecture, reliability, and scalability of Collinear’s AI agent improvement product offerings.
Design and operate multi-tenant, distributed backend systems that support enterprise customers.
Make platform-level decisions across infrastructure, runtimes, deployment models, and developer experience.
Translate customer needs and ambiguity into clear engineering priorities and durable platform capabilities.
Partner with founders and research leadership on technical strategy, roadmap, and tradeoffs.
Establish engineering standards and best practices as the platform and team scale.
Hire and mentor engineers over time as the organization grows.
We’re looking for someone who is deeply technical, product-minded, and comfortable operating with ambiguity.
You likely have:
7–10+ years of experience building backend systems, platforms, or cloud services.
Strong experience with distributed systems, infrastructure, and cloud-native architectures.
Hands-on experience with:
Python (primary language today)
Containers and orchestration (Docker, Kubernetes)
Cloud platforms (AWS, GCP, or Azure)
Experience building or operating B2B SaaS platforms at scale, including multitenancy and reliability concerns.
Comfort working directly with customers in a B2B setting, including handling escalations and ambiguous requirements.
Strong communication skills and sound technical judgment.
Experience in cloud or data infrastructure companies (e.g., AWS, GCP, Azure, Databricks, Snowflake, Datadog, Confluent).
Prior experience at an early-stage startup.
Some people management or hiring experience (not required).
Experience working on ML platforms, evaluation systems, or large-scale data pipelines.
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $200,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT THE ROLE\n\nAs a Member of Technical Staff (MTS - Staff Engineer) within the company, you will own the architecture, reliability, and evolution of Collinear’s AI agent improvement product offerings. This role is deeply hands-on and focused on backend systems, infrastructure, and distributed execution, with a strong emphasis on performance, scalability, and enterprise readiness.\n\nYou will be responsible for designing and operating the systems that support multiple customers, multiple deployment models, and an expanding set of evaluation and simulation scenarios. While people management and hiring are nice to have, the priority is technical leadership, architectural judgment, and execution in a fast-moving, customer-facing environment.\n\nThis role is ideal for someone who has built and operated cloud services at scale and wants to apply that experience to a complex, high-impact AI platform.\n\n\n\n\nWHAT YOU’LL DO\n\n - Own the architecture, reliability, and scalability of Collinear’s AI agent improvement product offerings.\n\n - Design and operate multi-tenant, distributed backend systems that support enterprise customers.\n\n - Make platform-level decisions across infrastructure, runtimes, deployment models, and developer experience.\n\n - Translate customer needs and ambiguity into clear engineering priorities and durable platform capabilities.\n\n - Partner with founders and research leadership on technical strategy, roadmap, and tradeoffs.\n\n - Establish engineering standards and best practices as the platform and team scale.\n\n - Hire and mentor engineers over time as the organization grows.\n \n \n\n\nABOUT YOU\n\nWe’re looking for someone who is deeply technical, product-minded, and comfortable operating with ambiguity.\n\nYou likely have:\n\n - 7–10+ years of experience building backend systems, platforms, or cloud services.\n\n - Strong experience with distributed systems, infrastructure, and cloud-native architectures.\n\n - Hands-on experience with:\n \n - Python (primary language today)\n \n - Containers and orchestration (Docker, Kubernetes)\n \n - Cloud platforms (AWS, GCP, or Azure)\n\n - Experience building or operating B2B SaaS platforms at scale, including multitenancy and reliability concerns.\n\n - Comfort working directly with customers in a B2B setting, including handling escalations and ambiguous requirements.\n\n - Strong communication skills and sound technical judgment.\n \n \n\n\nNICE TO HAVE\n\n - Experience in cloud or data infrastructure companies (e.g., AWS, GCP, Azure, Databricks, Snowflake, Datadog, Confluent).\n\n - Prior experience at an early-stage startup.\n\n - Some people management or hiring experience (not required).\n\n - Experience working on ML platforms, evaluation systems, or large-scale data pipelines.\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $200,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.\n\n"},{"id":"72bb3260-8663-4fe3-ae62-cefaac59ba1c","title":"MTS - Engineering","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-05-01T22:09:15.759+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/72bb3260-8663-4fe3-ae62-cefaac59ba1c","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/72bb3260-8663-4fe3-ae62-cefaac59ba1c/application","descriptionHtml":"At Collinear, we help teams fearlessly ship AI.
Frontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification.
SimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.
We are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.
Key responsibilities include:
Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)
Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability
Collaborate with cross-functional teams to define features, enhancements, and deliver product updates
Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes
Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks
Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.
Work with both SQL and NoSQL databases, optimizing queries for efficient data management
Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies
There are a few specific things we’ll be looking for that will help you succeed in this role:
Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field
Experience in full stack development with a focus on both frontend and backend technologies
Proficiency in JavaScript frameworks (React/Next.js) for frontend development
Strong backend development skills in Python (FastAPI) or similar languages
Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines
Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach
Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization
Experience in AI/ML or NLP/LLM projects is highly desirable
Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred
Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
Prior experience in top-tier technology companies or startups is a plus
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $150,000 to $300,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT COLLINEAR\n\nAt Collinear, we help teams fearlessly ship AI.\n\nFrontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification.\n\nSimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.\n\nKey responsibilities include:\n\n - Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)\n\n - Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability\n\n - Collaborate with cross-functional teams to define features, enhancements, and deliver product updates\n\n - Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes\n\n - Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks\n\n - Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.\n\n - Work with both SQL and NoSQL databases, optimizing queries for efficient data management\n\n - Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies\n\n\nABOUT YOU\n\nThere are a few specific things we’ll be looking for that will help you succeed in this role:\n\n - Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field \n\n - Experience in full stack development with a focus on both frontend and backend technologies\n\n - Proficiency in JavaScript frameworks (React/Next.js) for frontend development\n\n - Strong backend development skills in Python (FastAPI) or similar languages\n\n - Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines\n\n - Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach\n\n - Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization\n\n - Experience in AI/ML or NLP/LLM projects is highly desirable\n\n - Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred\n\n - Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment\n\n - Prior experience in top-tier technology companies or startups is a plus\n\n\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $150,000 to $300,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records."},{"id":"4b21f4d6-519c-42c9-b721-e73bbc81b3ad","title":"MTS - Research","department":"Research","team":"Research","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-04-30T17:43:08.385+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/4b21f4d6-519c-42c9-b721-e73bbc81b3ad","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/4b21f4d6-519c-42c9-b721-e73bbc81b3ad/application","descriptionHtml":"At Collinear, we help teams fearlessly ship AI.
We are looking for MTS - Research team member to help us build the data engine for frontier AI. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.
Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic and Agentic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.
High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.
Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.
Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT COLLINEAR\n\nAt Collinear, we help teams fearlessly ship AI.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for MTS - Research team member to help us build the data engine for frontier AI. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.\n\n\n\nRESPONSIBILITIES:\n\n - Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.\n\n - Programmatic and Agentic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.\n\n - Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.\n\n - Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.\n\n - Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.\n\n\n\nABOUT YOU\n\nWe are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.\n\n - Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.\n\n - Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.\n\n - ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.\n\n - Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.\n \n\n\nWHAT WILL MAKE YOU STAND OUT\n\n - Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.\n\n - Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.\n\n - Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.\n\n - Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.\n\n - Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.\n\n\n\n\nWHY JOIN COLLINEAR\n\n - Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.\n\n - High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.\n\n - Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.\n\n - Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.\n\n\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records."},{"id":"4d4af6b1-bfc7-4a28-9d86-5bab73e6e396","title":"MTS - Product","department":"Product","team":"Product","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-06-24T17:06:56.792+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/4d4af6b1-bfc7-4a28-9d86-5bab73e6e396","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/4d4af6b1-bfc7-4a28-9d86-5bab73e6e396/application","descriptionHtml":"As a Member of Technical Staff - Product, you will help decide what Collinear builds, and then build it. You’ll work directly with researchers and engineers at cutting-edge AI labs, identify their hardest problems, and turn those problems into products that advance how frontier agents are trained and evaluated.
This is a high-ownership, hands-on role at the intersection of Product, Engineering, and Research. You’ll shape Collinear’s product roadmap, prototype solutions, contribute code, run experiments, and work with the team to take ideas from ambiguous customer need to a product used in real research workflows.
Set the product strategy and internal roadmap so the team ships the environments, tasks, and evaluation data that address the hardest open problems at frontier labs
Work directly with researchers and engineers at leading AI labs, becoming deeply familiar with how they build, evaluate, and improve agents
Own important technical product areas such as agent environments, eval and quality systems, etc., from initial discovery through launch and iteration
Turn ambiguous research and customer needs into clear product bets, requirements, prototypes, and measurable outcomes
Drive projects from discovery through delivery. Coordinate implementation across research and engineering teams while contributing directly through prototypes, code changes, tests, technical reviews, and debugging when that is the fastest path forward.
Keep customers and cross-functional teams aligned through clear roadmaps, demos, and playbooks, communicating progress, tradeoffs, risks, and decisions clearly
You have strong product judgment and can create clarity from loosely defined technical problems
You are technically fluent enough to navigate unfamiliar codebases, understand system architecture and data flows, debug failures, review pull requests, and make focused implementation changes
You have worked on developer tools, AI infrastructure, evaluation systems, data platforms, workflow products, or similarly technical products
You can reason carefully about quality: what should be measured, what evidence proves something works
You communicate effectively with researchers, engineers, customers, and company leadership, adjusting the level of technical detail without losing precision
You operate well in a fast-moving environment where the roadmap evolves as new customer and model evidence emerges
You are comfortable being accountable for the outcome even when you are not the sole implementer
You are an unusually effective user of AI development tools and know that generated work still requires strong scoping, review, testing, and judgment
Experience with AI agents, evaluations, benchmarks, reinforcement-learning environments, task generation, or verifier design.
Familiarity with computer-use or MCP-based agent environments.
Experience designing quality or release systems for technically complex customer deliverables.
Experience working directly with frontier AI labs, applied research teams, or enterprise technical customers.
Early-stage startup experience spanning product, engineering, operations, and customer delivery.
Collinear's Internship program is designed for PhD students who are eligible to do a 12-week internship. Start dates are flexible and can be extended.
As an MTS - Research Scientist (Applied Scientist), you will help build the data engine for frontier AI. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.
Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic and Agentic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
Create. Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.
High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.
Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.
Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.
We are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.
Key responsibilities include:
Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)
Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability
Collaborate with cross-functional teams to define features, enhancements, and deliver product updates
Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes
Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks
Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.
Work with both SQL and NoSQL databases, optimizing queries for efficient data management
Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies
There are a few specific things we’ll be looking for that will help you succeed in this role:
Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field
Experience in full stack development with a focus on both frontend and backend technologies
Proficiency in JavaScript frameworks (React/Next.js) for frontend development
Strong backend development skills in Python (FastAPI) or similar languages
Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines
Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach
Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization
Experience in AI/ML or NLP/LLM projects is highly desirable
Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred
Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
Prior experience in top-tier technology companies or startups is a plus
We are looking for a Member of Technical Staff (MTS - Research) to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics.
Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain \"command-line first\" and developer-friendly.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Collinear.AI is seeking a Member of Technical Staff (Applied Scientist) with deep expertise in engineering sciences to work at the frontier of AI-accelerated simulation. In this role, you will collaborate with customers and internal research teams to build, test, and deploy AI Physics Models.
You will contribute across the full stack: curating high-fidelity simulation datasets, training and evaluating physics-informed models, and delivering production-grade AI solutions directly to engineering teams. Key target domains include computational fluid dynamics (CFD), structural mechanics, semiconductor design, multi-physics modeling, and digital twins.
Working cross-functionally across research, product, and client-facing teams, you will ensure models meet rigorous real-world engineering standards—not just theoretical benchmark metrics.
Execute Simulation Campaigns: Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
Train & Validate Models: Train AI models on physics datasets and conduct rigorous evaluations of coverage, accuracy, and output quality against industrial validation standards.
Build Infrastructure & Tooling: Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.
Integrate LLMs & Workflows: Architect agentic workflows and Retrieval-Augmented Generation (RAG) systems that seamlessly connect LLMs with engineering simulation pipelines.
Research Collaboration: Partner closely with the research team to analyze training runs, diagnose failure modes, and address data sparsity or architecture bottlenecks.
Technical Project Management: Lead research initiatives and manage technical communications with external engineering teams.
Education: Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field.
Technical Mastery: Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences.
Framework Proficiency: Hands-on experience implementing and training deep learning models.
Software Engineering: Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments.
Communication: Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders.
Ownership & Mindset: Self-directed operator who thrives with autonomy, maintains a low-ego approach to collaboration, and excels in fast-paced environments at the intersection of simulation and ML.
Hands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
Direct experience applying machine learning to physics simulations or surrogate modeling (e.g., Neural Operators, Physics-Informed Neural Networks).
Track record of automating large-scale simulation workloads on HPC clusters.
Meaningful contributions to large-scale open-source projects or production codebases.
Published research in top-tier machine learning (NeurIPS, ICLR, ICML) or computational engineering conferences/journals.
Strong software engineering discipline, including static typing, unit testing, and CI/CD maintenance.
We are seeking an Applied Scientist (Robotics Simulation) to design and build the physics-solving engine behind Collinear’s synthetic data generation platform. In this role, you will architect and optimize a next-generation simulation stack engineered purely for maximum data throughput, procedural environmental scale, and physical fidelity.
You will collaborate closely with GPU optimization engineers, procedural scene generation teams, and AI researchers pushing the limits of model training on synthetic data. The ideal candidate brings deep physics simulation expertise and hands-on experience implementing high-performance solvers on modern parallel GPU architectures.
Physics Solver Innovation: Improve and develop advanced physics solvers and numerical modeling methods for high-degree-of-freedom interactions, complex contact dynamics, deformables, and fluids.
Training Data Optimization: Partner with team members to design simulation environments, domain randomization strategies, and physics parameter ranges that yield high-utility training datasets for downstream AI models.
Procedural Scene & Scale Integration: Collaborate with scene-generation engineers to scale environment variations, physical property distributions, and dynamic multi-agent interactions across billions of simulation steps.
Performance Profiling: Profile and optimize simulation execution at scale, minimizing latency and memory overhead in multi-GPU, parallelized rollout pipelines.
Technical Leadership: Help shape Collinear’s long-term roadmap for high-fidelity physical modeling, differentiable simulation, and synthetic data engine architecture.
Education: MS or PhD in Physics, Computer Science, Applied Math, Engineering, or equivalent hands-on industry experience in physics-based simulation.
Physics Engine Expertise: Strong track record of developing or extending high-fidelity physics engines, rigid-body dynamics solvers, or constraint-based systems. Experience with deformables, fluids, soft bodies, or differentiable simulation is a plus.
Numerical Methods & Dynamics: Deep understanding of classical mechanics, contact modeling, constraint solvers, integrators, and managing accuracy-vs-speed trade-offs for large-scale computation.
GPU Programming: Advanced expertise in CUDA and GPU optimization, with a proven ability to accelerate numerical algorithms on parallel hardware architectures.
Systems Engineering: High proficiency in C++ and Python, with a track record of building reliable, high-throughput software used in production data or ML pipelines.
ML Infrastructure Familiarity: Strong grasp of how ML frameworks consume simulation outputs (e.g., vectorized environments, massively parallel rollouts, synthetic dataset generation).
Fidelity Intuition: Deep intuition for physical realism and dataset distribution drift—understanding how synthetic physical data behaves and how to model environmental variance without relying on physical hardware collection.
Track Record: Publications, open-source contributions, or shipped commercial systems in numerical computing, graphics, physics simulation, or synthetic data generation.
We’re looking for a Member of Technical Staff, Research focused on drug discovery to help advance AI’s ability to reason about chemistry and solve problems that matter to discovery teams.
You’ll bring deep expertise in computational and medicinal chemistry, along with hands-on experience building and evaluating machine learning models. Your work will connect model development to real scientific challenges: designing compounds, planning syntheses, interpreting experimental results, and navigating tradeoffs across potency, selectivity, and ADMET properties.
This is a hands-on research role with broad ownership. You’ll develop datasets, evaluations, and reinforcement learning environments; run experiments with our AI researchers; and help decide which problems we should tackle next. As an early contributor to this effort, you’ll help define both the research agenda and how we work.
Own research from idea to result. Identify high-value drug discovery problems, define a research approach, build prototypes, and test whether they improve model capabilities.
Build environments that teach AI chemistry. Develop reinforcement learning tasks with meaningful rewards, reliable grading, and safeguards against shortcuts and data leakage.
Turn scientific data into useful training signals. Curate and structure data from literature, patents, experimental results, and other relevant sources.
Measure progress on real discovery tasks. Build evaluations for molecular design, reaction prediction, synthesis planning, structure elucidation, and multiparameter optimization.
Run experiments and follow the evidence. Work closely with AI researchers and engineers to train models, investigate failures, and turn findings into the next round of improvements.
Keep the science grounded. Assess computational results against experimental evidence and collaborate with scientific partners to validate promising approaches.
Build for a growing team. Create reproducible workflows, document key decisions, and communicate results clearly so others can build on your work.
A PhD in chemistry, computational chemistry, cheminformatics, or a related field—or equivalent research experience.
Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, structure–activity relationships, and the practical tradeoffs involved in drug discovery.
Strong Python skills and a solid foundation in machine learning, with hands-on experience training and evaluating models.
Scientific judgment: you can distinguish a promising benchmark result from a meaningful advance in a discovery workflow.
Comfort working through ambiguity, choosing priorities, and taking a project from an open question to a concrete result.
A collaborative approach, clear communication, and the willingness to work across research and engineering as the team grows.
Reinforcement learning, large language models, scientific agents, or chemistry-focused evaluations and benchmarks.
Contributions to drug discovery programs in close collaboration with medicinal or synthetic chemists.
Experience curating reaction, bioactivity, ADMET, or spectroscopic datasets, with attention to provenance, quality, and data leakage.
Familiarity with tools and methods such as RDKit, molecular docking, molecular dynamics, free energy calculations, or quantum chemistry.
Hands-on synthetic or medicinal chemistry experience at the bench.
Publications, patents, open-source tools, or deployed research systems that demonstrate scientific impact.
You don’t need to check every box. We’re interested in people who bring depth, curiosity, and the drive to build useful things—and who want to help shape what comes next.
Collinear AI builds environments, tasks, and evaluations that help frontier AI models improve at real work. We are growing our cybersecurity team and looking for someone who can turn practical security problems into environments where AI agents can investigate, act, and learn.
We recently released CWE-bench, a defensive cybersecurity benchmark with 100 held-out audit-and-patch tasks across 54 weakness types. Agents must find and fix vulnerabilities in real codebases, with checks that confirm the vulnerability is resolved and existing functionality still works. You will help build what comes next: richer cybersecurity environments, realistic tasks, and reliable ways to measure whether agents succeed.
You will own work from the initial security scenario through the runnable environment, task instructions, reference solution, and verifier. We are looking for hands-on security knowledge, strong programming skills, and curiosity about how AI agents fail.
Deep cybersecurity expertise is enough to get started—you do not need prior AI or machine-learning experience. If you know how to investigate vulnerabilities, reason about security failures, and verify that a fix works, we want to hear from you. We will teach you our AI tooling and evaluation workflows.
Build cybersecurity environments. Create isolated, reproducible environments with real repositories, applications, services, logs, and access controls. Make them straightforward to launch, reset, and evaluate at scale.
Turn security work into tasks. Design scenarios around vulnerability discovery and remediation, application and API security, authentication and authorization, incident investigation, and system hardening. Define what the agent knows, which tools it can use, and what it must accomplish.
Develop reference solutions and verifiers. Reproduce the underlying issue, implement a valid solution, and write checks that distinguish a real fix from a superficial workaround. Verify that attacks fail after remediation while legitimate behavior continues to work.
Test the tests. Challenge graders with incomplete fixes, disabled features, hard-coded answers, and other shortcuts. Keep hidden solutions and test data out of the agent's environment, and separate genuine model failures from broken infrastructure.
Run and analyze agents. Evaluate frontier models, inspect their tool calls and code changes, and explain where their security reasoning or execution breaks down. Use those findings to improve task coverage and difficulty.
Contribute to benchmarks and research. Work with researchers and engineers to turn strong environments into evaluation suites and training data. Review other contributors' tasks and help document results for future releases.
Practical depth in at least one area of cybersecurity, such as application security, vulnerability research, penetration testing, systems security, cloud security, or incident response. Evidence can come from internships, research, open-source work, bug bounties, CTFs, or independent projects.
The ability to read unfamiliar code, reproduce a security issue, understand its root cause, and implement or assess a fix.
Strong programming skills in Python and at least one language used in the systems you investigate, such as C/C++, Go, Java, JavaScript/TypeScript, or Rust.
Comfort with Linux, Git, containers, debugging, and automated testing.
Clear technical writing and careful judgment about what an evaluation does and does not demonstrate. You can explain why a task is realistic and why its grading is trustworthy.
Curiosity about applying your cybersecurity expertise to AI, and a willingness to learn how to evaluate tool-using agents. Prior experience with LLMs is not expected.
Recent graduates and early-career engineers or researchers are encouraged to apply. A PhD, professional certification, or previous role at an AI lab is not required. We value demonstrated ability and the quality of your work.
Disclosed vulnerabilities, accepted security patches, strong CTF results, or useful security tools and write-ups.
Experience with fuzzing, static or dynamic analysis, reverse engineering, or building security labs.
Familiarity with CWE and OWASP classifications and how they relate to concrete software failures.
Experience building agent evaluations, adversarial tests, prompt-injection defenses, or reinforcement-learning environments.
A repository audit where an agent must discover and repair an authorization flaw without being told where it is.
A small service environment where an agent investigates suspicious activity from logs and configuration, then applies and verifies a remediation.
An LLM application where an agent must repair a prompt-injection or tool-permission weakness while preserving legitimate functionality.
*When applying, include a project, code sample, security write-up, or research artifact that shows how you investigate a problem and establish that your solution works.
","descriptionPlain":"Collinear AI builds environments, tasks, and evaluations that help frontier AI models improve at real work. We are growing our cybersecurity team and looking for someone who can turn practical security problems into environments where AI agents can investigate, act, and learn.\n\nWe recently released CWE-bench https://cwe-bench.com/, a defensive cybersecurity benchmark with 100 held-out audit-and-patch tasks across 54 weakness types. Agents must find and fix vulnerabilities in real codebases, with checks that confirm the vulnerability is resolved and existing functionality still works. You will help build what comes next: richer cybersecurity environments, realistic tasks, and reliable ways to measure whether agents succeed.\n\nYou will own work from the initial security scenario through the runnable environment, task instructions, reference solution, and verifier. We are looking for hands-on security knowledge, strong programming skills, and curiosity about how AI agents fail.\n\n\nDeep cybersecurity expertise is enough to get started—you do not need prior AI or machine-learning experience. If you know how to investigate vulnerabilities, reason about security failures, and verify that a fix works, we want to hear from you. We will teach you our AI tooling and evaluation workflows.\n\n\n\nWHAT YOU WILL DO\n\n - Build cybersecurity environments. Create isolated, reproducible environments with real repositories, applications, services, logs, and access controls. Make them straightforward to launch, reset, and evaluate at scale.\n\n - Turn security work into tasks. Design scenarios around vulnerability discovery and remediation, application and API security, authentication and authorization, incident investigation, and system hardening. Define what the agent knows, which tools it can use, and what it must accomplish.\n\n - Develop reference solutions and verifiers. Reproduce the underlying issue, implement a valid solution, and write checks that distinguish a real fix from a superficial workaround. Verify that attacks fail after remediation while legitimate behavior continues to work.\n\n - Test the tests. Challenge graders with incomplete fixes, disabled features, hard-coded answers, and other shortcuts. Keep hidden solutions and test data out of the agent's environment, and separate genuine model failures from broken infrastructure.\n\n - Run and analyze agents. Evaluate frontier models, inspect their tool calls and code changes, and explain where their security reasoning or execution breaks down. Use those findings to improve task coverage and difficulty.\n\n - Contribute to benchmarks and research. Work with researchers and engineers to turn strong environments into evaluation suites and training data. Review other contributors' tasks and help document results for future releases.\n\n\n\nWHO WE ARE LOOKING FOR\n\n - Practical depth in at least one area of cybersecurity, such as application security, vulnerability research, penetration testing, systems security, cloud security, or incident response. Evidence can come from internships, research, open-source work, bug bounties, CTFs, or independent projects.\n\n - The ability to read unfamiliar code, reproduce a security issue, understand its root cause, and implement or assess a fix.\n\n - Strong programming skills in Python and at least one language used in the systems you investigate, such as C/C++, Go, Java, JavaScript/TypeScript, or Rust.\n\n - Comfort with Linux, Git, containers, debugging, and automated testing.\n\n - Clear technical writing and careful judgment about what an evaluation does and does not demonstrate. You can explain why a task is realistic and why its grading is trustworthy.\n\n - Curiosity about applying your cybersecurity expertise to AI, and a willingness to learn how to evaluate tool-using agents. Prior experience with LLMs is not expected.\n\nRecent graduates and early-career engineers or researchers are encouraged to apply. A PhD, professional certification, or previous role at an AI lab is not required. We value demonstrated ability and the quality of your work.\n\n\n\nNICE TO HAVE\n\n - Disclosed vulnerabilities, accepted security patches, strong CTF results, or useful security tools and write-ups.\n\n - Experience with fuzzing, static or dynamic analysis, reverse engineering, or building security labs.\n\n - Familiarity with CWE and OWASP classifications and how they relate to concrete software failures.\n\n - Experience building agent evaluations, adversarial tests, prompt-injection defenses, or reinforcement-learning environments.\n\n\n\n*EXAMPLES OF WHAT YOU MIGHT BUILD\n\n - A repository audit where an agent must discover and repair an authorization flaw without being told where it is.\n\n - A small service environment where an agent investigates suspicious activity from logs and configuration, then applies and verifies a remediation.\n\n - An LLM application where an agent must repair a prompt-injection or tool-permission weakness while preserving legitimate functionality.\n\n*When applying, include a project, code sample, security write-up, or research artifact that shows how you investigate a problem and establish that your solution works."},{"id":"3476fa4b-d845-4c09-8b61-fdf31738946d","title":"MTS - Engineering (Data Infrastructure)","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[{"location":"Sunnyvale, California","address":{"postalAddress":{"addressCountry":"United States"}}}],"publishedAt":"2026-10-01T02:41:05.838+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/3476fa4b-d845-4c09-8b61-fdf31738946d","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/3476fa4b-d845-4c09-8b61-fdf31738946d/application","descriptionHtml":"As a Member of Technical Staff - Engineering (Data Infrastructure), you will own the systems that turn large, real-world datasets into data Collinear can build on. Our environments are grounded in terabytes of data, spread across archives, spreadsheets, email, PDFs, and scanned documents. The pace at which we process these determines how fast we deliver to frontier labs.
This is a hands-on role at the intersection of algorithms, distributed systems, and data quality. Many of our hardest problems, such as linking related records across millions of files, don't split up neatly, and you will define how we solve them at scale.
Build pipelines that process multi-terabyte datasets in parallel across archives, spreadsheets, email, PDFs, and scanned documents
Design graph-based systems that link related records, such as the same person or company appearing across millions of files
Build fast string and pattern search over large, heterogeneous datasets
Profile and remove bottlenecks, and decide how to split work that doesn't parallelize neatly
Transform data for downstream use, including consistently replacing sensitive fields across files
Define how we measure data quality, and build review tools so the team can catch and fix errors without reprocessing everything
Assess new datasets and filter out low-quality data before it reaches our environments
You have 5+ years of experience building data-intensive systems in production
You have processed large datasets in parallel with frameworks such as Apache Spark, Ray, or Dask, and know when to design your own
You have a strong command of graph algorithms, and experience using them to transform large amounts of data
You have built efficient string matching and pattern search at scale, such as fuzzy matching or indexing
You make sound tradeoffs between accuracy, speed, and cost, and can explain them clearly
Experience with entity resolution or record linkage
Experience with NLP or LLM-based information extraction
OCR or document processing experience, including poor scans and handwriting
Experience in a systems language such as Rust, C++, or Go
Experience with regulated or sensitive data, such as financial or healthcare records
As a Member of Technical Staff - Engineering (Security), you will own security across Collinear's infrastructure, products, and customer data. We build RL environments and training data for frontier AI labs and large enterprises. They trust us with sensitive data and access to their systems, and many of our environments run AI agents that are being trained to find and exploit vulnerabilities.
This is a senior, hands-on role. You will work alongside our researchers and engineering team to set the direction for, and implement, new security measures.
Harden the sandboxes where AI agents run untrusted code, including cyber tasks where agents are actively trying to break out
Own AWS security, including IAM design, least-privilege access, network boundaries, and short-lived credentials in place of long-lived keys
Build detection and response across our cloud and Kubernetes environments, and tune alerts so the team acts on them
Protect customer data from the moment it arrives to the moment we deliver, including access controls, secure delivery, and PII removal at terabyte scale
Lead customer security reviews and compliance work, from security questionnaires and supplier onboarding to SOC 2
Secure our software supply chain, including dependencies, CI/CD, secrets, and our GitHub organization
Set guardrails for engineers and coding agents that work directly against production infrastructure
Test our systems the way an attacker would, and fix what you find
You have 8+ years of experience in security engineering, much of it securing cloud infrastructure in production
You have deep hands-on experience with AWS security, including IAM, CloudTrail, GuardDuty, KMS, and VPC design
You have an offensive security background, such as penetration testing, red teaming, or vulnerability research, and use it to decide what to defend first
You have isolated untrusted workloads with containers or microVMs, using tools such as gVisor, Firecracker, or Kata Containers, and know where that isolation tends to fail
You write production-quality code, ideally in Python, and prefer automation to manual process
You have built or grown a security program at a startup, and know how to prioritize when you can't fix everything at once
You explain risk clearly to engineers, leadership, and customers' security teams
You will work closely with marketing leadership to plan, execute, and optimize digital marketing campaigns that build awareness, generate demand, and convert engaged audiences into qualified leads. You will own campaign execution across paid and organic social media—including X (Twitter), LinkedIn, and other relevant platforms—as well as email and landing pages.
This is a hands-on role for an experienced digital marketer who understands marketing funnels, uses data to identify where audiences drop off, and turns those insights into stronger campaign performance. You’ll combine creative experimentation with rigorous measurement to help grow our business in the fast-moving world of AI.
Own digital marketing campaigns from planning through optimization. Define audiences, objectives, channel strategy, budgets, timelines, and success metrics, then manage launch, monitoring, and reporting.
Build and optimize marketing funnels. Map the journey from first impression through qualified lead, measure passthrough rates between each stage, and address the biggest sources of drop-off.
Manage paid and organic social campaigns. Execute and continuously improve campaigns across X, LinkedIn, and other relevant platforms, tailoring creative, messaging, targeting, and distribution to each channel.
Run structured experiments. Test audiences, creative, copy, offers, landing pages, and calls to action to improve conversion rates and lead quality.
Improve conversion across campaign touchpoints. Build and refine landing pages, forms, email sequences, nurture programs, and retargeting campaigns.
Own campaign measurement and reporting. Maintain reliable tracking links, conversion events, dashboards, and CRM data so campaign results connect to downstream outcomes.
Optimize spend and performance. Monitor click-through rates, funnel passthrough rates, cost per lead, cost per qualified lead, and pipeline contribution, using results to guide budget allocation.
Partner across teams. Work with marketing leadership, creative contributors, and sales to develop campaign assets, align on lead qualification, and incorporate feedback into future campaigns.
Experience independently managing and optimizing digital marketing campaigns, including paid social campaigns across X and LinkedIn.
A strong understanding of marketing funnels and stage-to-stage pass-through rates, with the ability to diagnose conversion bottlenecks and improve performance.
A track record of using testing, audience segmentation, and budget optimization to deliver measurable results.
Hands-on experience with advertising platforms, web analytics, conversion tracking, CRM systems, and marketing automation tools.
The ability to translate campaign data into clear recommendations and distinguish engagement metrics from meaningful business outcomes.
Strong project management, writing, and collaboration skills, with attention to both creative quality and execution details.
An interest in AI and the ability to communicate new products and ideas clearly to relevant audiences.
Campaigns launch consistently with clear audiences, goals, budgets, tracking, and follow-up plans.
Funnel pass-through rates improve as you identify and resolve points of friction.
Social campaigns—including those on X—generate relevant traffic and qualified leads.
Campaign efficiency and lead quality improve through ongoing testing and optimization.
Reporting clearly connects channel activity and spend to conversions, qualified leads, and pipeline.
Successful experiments inform future campaigns and where we invest.
Collinear is a data research lab that builds simulated worlds to train and evaluate AI agents. We partner with frontier labs to build high-signal custom RL environments, tasks, and graders for coding, knowledge work, and science domains. Collinear is also a pioneer in frontier evals: CWE-bench, MatFlowBench, and FrontierCUA
We’re looking for a Chief of Staff to partner with the CEO and help build the company behind that work. You’ll turn our most important priorities into decisions, coordinated execution, and measurable results. One week, you might evaluate a new market opportunity; the next, you might improve how we deliver a customer project or prepare for an investor conversation.
This is a hands-on role for someone who can move between strategy and execution, earn the trust of technical and commercial teams, and take ownership when the path is unclear. You’ll build enough understanding of our technology, customers, and economics to challenge assumptions and make sound trade-offs.
Drive company priorities. Work with the CEO and functional leaders to define the highest-impact goals, assign owners, resolve dependencies, and follow through on commitments.
Own critical projects end to end. Take ambiguous problems from diagnosis to delivery across research, engineering, customer delivery, and go-to-market. Establish what success means, do the work, and build a repeatable process where it helps.
Build our operating cadence. Run focused planning and leadership reviews, maintain a useful view of company metrics, and make decisions and accountability clear without adding unnecessary process.
Sharpen strategic decisions. Combine customer conversations, market research, and quantitative analysis to assess opportunities, pricing, partnerships, and resource allocation. Write clear recommendations that make tradeoffs explicit.
Extend the CEO’s effectiveness. Prepare decision briefs, investor and board materials, and internal communications. Ensure important customer, partner, and investor conversations lead to concrete next steps.
Make us more effective with AI. Build and improve AI-assisted workflows for research, reporting, and operations. Evaluate their accuracy and usefulness, and keep human judgment where it matters.
Help the organization scale. Identify gaps in how we hire, onboard, share information, and work together; solve the ones that most constrain the team’s progress.
A track record of taking difficult, loosely defined projects through to measurable results in a fast-moving environment. Relevant paths include startup operations, product, business operations, consulting with execution experience, or building a company yourself.
Strong analytical judgment: you can work with data, understand business economics, test assumptions, and explain your conclusions clearly.
Technical curiosity and fluency. You can learn how AI training, evaluations, and agent workflows work and collaborate credibly with researchers and engineers. You don’t need to be an ML researcher.
Excellent writing and communication. You distill complex information, surface disagreement constructively, and help teams reach decisions.
Initiative, discretion, and dependable follow-through. You handle sensitive information thoughtfully and influence across teams through trust and sound judgment.
Practical experience using AI tools to improve your work, including checking their outputs and knowing their limits.
Experience with AI infrastructure, enterprise customers, data operations, or a technical founder-led company is especially useful. We care more about what you’ve built and delivered than a particular title, degree, or career path.
You understand our customers, product, delivery process, and business priorities well enough to identify the most important operational bottlenecks.
You’ve established a lightweight planning and review cadence with clear owners, actionable metrics, and reliable follow-through.
You’ve delivered a meaningful cross-functional project and improved at least one recurring workflow using AI, with a measurable benefit.
The CEO and team will have clearer priorities, faster decisions, and more time for their highest-impact work.
You’ll work close to the frontier of AI capability development and help shape how a technical company grows. The role offers broad responsibility, direct partnership with the CEO, and the opportunity to build systems and lead initiatives whose impact you can see.
","descriptionPlain":"ABOUT COLLINEAR\n\nCollinear is a data research lab that builds simulated worlds to train and evaluate AI agents. We partner with frontier labs to build high-signal custom RL environments, tasks, and graders for coding, knowledge work, and science domains. Collinear is also a pioneer in frontier evals: CWE-bench https://cwe-bench.com/, MatFlowBench https://matflowbench.com/, and FrontierCUA https://frontiercua.com/leaderboard/\n\n\n\n\nTHE ROLE\n\nWe’re looking for a Chief of Staff to partner with the CEO and help build the company behind that work. You’ll turn our most important priorities into decisions, coordinated execution, and measurable results. One week, you might evaluate a new market opportunity; the next, you might improve how we deliver a customer project or prepare for an investor conversation.\n\nThis is a hands-on role for someone who can move between strategy and execution, earn the trust of technical and commercial teams, and take ownership when the path is unclear. You’ll build enough understanding of our technology, customers, and economics to challenge assumptions and make sound trade-offs.\n\n\n\n\nWHAT YOU’LL DO\n\n - Drive company priorities. Work with the CEO and functional leaders to define the highest-impact goals, assign owners, resolve dependencies, and follow through on commitments.\n\n - Own critical projects end to end. Take ambiguous problems from diagnosis to delivery across research, engineering, customer delivery, and go-to-market. Establish what success means, do the work, and build a repeatable process where it helps.\n\n - Build our operating cadence. Run focused planning and leadership reviews, maintain a useful view of company metrics, and make decisions and accountability clear without adding unnecessary process.\n\n - Sharpen strategic decisions. Combine customer conversations, market research, and quantitative analysis to assess opportunities, pricing, partnerships, and resource allocation. Write clear recommendations that make tradeoffs explicit.\n\n - Extend the CEO’s effectiveness. Prepare decision briefs, investor and board materials, and internal communications. Ensure important customer, partner, and investor conversations lead to concrete next steps.\n\n - Make us more effective with AI. Build and improve AI-assisted workflows for research, reporting, and operations. Evaluate their accuracy and usefulness, and keep human judgment where it matters.\n\n - Help the organization scale. Identify gaps in how we hire, onboard, share information, and work together; solve the ones that most constrain the team’s progress.\n\n\n\n\nWHAT YOU BRING\n\n - A track record of taking difficult, loosely defined projects through to measurable results in a fast-moving environment. Relevant paths include startup operations, product, business operations, consulting with execution experience, or building a company yourself.\n\n - Strong analytical judgment: you can work with data, understand business economics, test assumptions, and explain your conclusions clearly.\n\n - Technical curiosity and fluency. You can learn how AI training, evaluations, and agent workflows work and collaborate credibly with researchers and engineers. You don’t need to be an ML researcher.\n\n - Excellent writing and communication. You distill complex information, surface disagreement constructively, and help teams reach decisions.\n\n - Initiative, discretion, and dependable follow-through. You handle sensitive information thoughtfully and influence across teams through trust and sound judgment.\n\n - Practical experience using AI tools to improve your work, including checking their outputs and knowing their limits.\n\nExperience with AI infrastructure, enterprise customers, data operations, or a technical founder-led company is especially useful. We care more about what you’ve built and delivered than a particular title, degree, or career path.\n\n\n\n\nWHAT SUCCESS LOOKS LIKE IN YOUR FIRST 90 DAYS\n\n - You understand our customers, product, delivery process, and business priorities well enough to identify the most important operational bottlenecks.\n\n - You’ve established a lightweight planning and review cadence with clear owners, actionable metrics, and reliable follow-through.\n\n - You’ve delivered a meaningful cross-functional project and improved at least one recurring workflow using AI, with a measurable benefit.\n\n - The CEO and team will have clearer priorities, faster decisions, and more time for their highest-impact work.\n\n\n\n\nWHY COLLINEAR\n\nYou’ll work close to the frontier of AI capability development and help shape how a technical company grows. The role offers broad responsibility, direct partnership with the CEO, and the opportunity to build systems and lead initiatives whose impact you can see."},{"id":"482e84cd-240c-499c-928c-1d87c88cf95c","title":"MTS - Research (Kernels & Hardware RL)","department":"Research","team":"Research","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-10-29T04:40:33.755+00:00","isListed":true,"isRemote":null,"workplaceType":null,"address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/482e84cd-240c-499c-928c-1d87c88cf95c","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/482e84cd-240c-499c-928c-1d87c88cf95c/application","descriptionHtml":"At Collinear, we help teams fearlessly ship AI.
We are looking for an MTS - Research (Kernels & Hardware RL) team member to help us build the data engine for frontier AI. You will develop high-fidelity reinforcement learning environments and evaluation stacks that teach advanced agents to write efficient GPU kernels and solve hardware optimization problems. You will own environments end to end, from selecting meaningful tasks and reference implementations to building reliable scoring and preventing reward hacking.
Build Agentic Environments: Design diverse, executable tasks for GPU and accelerator kernels, low-level systems programming, and hardware-aware optimization. Extend into FPGA, RTL, or simulation workflows where your expertise can create meaningful challenges.
Programmatic and Agentic Verification: Build reproducible correctness tests and performance graders, benchmark against trusted implementations, and harden execution sandboxes against timing exploits, test leakage, and reward hacking.
Close the Loop: Analyze agent trajectories and failure modes, curate high-signal tasks and data, and collaborate on post-training experiments that improve real capability across unseen workloads and hardware constraints.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
Scale the Infrastructure: Build reliable compilation, execution, profiling, and evaluation pipelines that support repeated experiments across hardware targets.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in CS, Electrical or Computer Engineering, or a related technical field, or demonstrated proof of work through significant open-source contributions or industry experience.
Engineering Rigor: Strong C/C++ and CUDA or a comparable kernel language, alongside production-quality Python. You have built and profiled kernels and understand memory hierarchy, data movement, parallelism, occupancy, and numerical correctness.
ML Fluency: Hands-on experience with LLMs and a principled understanding of how they are evaluated and optimized. You can translate a systems problem into an executable task with clear feedback.
Empirical Mindset: Experience conducting reproducible performance experiments, inspecting profiler results, and distinguishing real improvements from measurement noise or benchmark shortcuts.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Depth in Triton, CUTLASS, LLVM/MLIR, GPU architectures, vector ISAs, FPGA/HLS, RTL/HDL, EDA tools, or architecture simulators. Experience building RL environments, agent harnesses, or robust evaluation infrastructure.
Proven Track Record: A history of shipping high-performance kernels, compiler or hardware tooling, influential systems or ML research, or high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.
High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.
Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.
Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT COLLINEAR\n\nAt Collinear, we help teams fearlessly ship AI.\n\n \n\n\nABOUT THE ROLE\n\nWe are looking for an MTS - Research (Kernels & Hardware RL) team member to help us build the data engine for frontier AI. You will develop high-fidelity reinforcement learning environments and evaluation stacks that teach advanced agents to write efficient GPU kernels and solve hardware optimization problems. You will own environments end to end, from selecting meaningful tasks and reference implementations to building reliable scoring and preventing reward hacking.\n\n\nRESPONSIBILITIES:\n\n - Build Agentic Environments: Design diverse, executable tasks for GPU and accelerator kernels, low-level systems programming, and hardware-aware optimization. Extend into FPGA, RTL, or simulation workflows where your expertise can create meaningful challenges.\n\n - Programmatic and Agentic Verification: Build reproducible correctness tests and performance graders, benchmark against trusted implementations, and harden execution sandboxes against timing exploits, test leakage, and reward hacking.\n\n - Close the Loop: Analyze agent trajectories and failure modes, curate high-signal tasks and data, and collaborate on post-training experiments that improve real capability across unseen workloads and hardware constraints.\n\n - Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.\n\n - Scale the Infrastructure: Build reliable compilation, execution, profiling, and evaluation pipelines that support repeated experiments across hardware targets.\n\n\n\nABOUT YOU\n\nWe are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.\n\n - Technical Foundation: A Bachelor’s, Master’s, or PhD in CS, Electrical or Computer Engineering, or a related technical field, or demonstrated proof of work through significant open-source contributions or industry experience.\n\n - Engineering Rigor: Strong C/C++ and CUDA or a comparable kernel language, alongside production-quality Python. You have built and profiled kernels and understand memory hierarchy, data movement, parallelism, occupancy, and numerical correctness.\n\n - ML Fluency: Hands-on experience with LLMs and a principled understanding of how they are evaluated and optimized. You can translate a systems problem into an executable task with clear feedback.\n\n - Empirical Mindset: Experience conducting reproducible performance experiments, inspecting profiler results, and distinguishing real improvements from measurement noise or benchmark shortcuts.\n\n\nWHAT WILL MAKE YOU STAND OUT\n\n - Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.\n\n - Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.\n\n - Domain Expertise: Depth in Triton, CUTLASS, LLVM/MLIR, GPU architectures, vector ISAs, FPGA/HLS, RTL/HDL, EDA tools, or architecture simulators. Experience building RL environments, agent harnesses, or robust evaluation infrastructure.\n\n - Proven Track Record: A history of shipping high-performance kernels, compiler or hardware tooling, influential systems or ML research, or high-impact open-source projects.\n\n - Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.\n\n \n\n\nWHY JOIN COLLINEAR\n\n - Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.\n\n - High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.\n\n - Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.\n\n - Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.\n\n \n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records."}],"apiVersion":"1"}