TL;DR: If you:
obsess over how intelligent systems learn, adapt, and improve over time;
have a track record of exceptional research or engineering achievement;
move effortlessly between papers, experiments, and production code;
turn half-baked ideas into working systems…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI.
About the opportunity:
We’re seeking an exceptional AI Research Scientist to join our small team of elite researchers and help us push the boundaries of AI applied to the physical world. This role blends cutting-edge AI research with hands-on engineering, and is ideal for someone who thrives at the intersection of ideas and implementation.
About the role:
Research and develop approaches for continual learning, fast adaptation, memory formation, knowledge retention, and transfer learning in large-scale AI systems.
Own the full development pipeline from data generation to evaluation to product integration.
Contribute to both research strategy and technical implementation—this is a hands-on role. Have ownership over your own applied research stream.
Investigate topics such as continual learning, online learning, test-time adaptation, memory architectures, meta-learning, self-improvement, and knowledge consolidation.
About you:
A PhD (or equivalent experience) in Computer Science, Robotics, Engineering, Math, or a related field.
Have conducted research in one or more areas such as continual learning, lifelong learning, meta-learning, online learning, reinforcement learning, memory systems, model adaptation, transfer learning, or related fields.
Are fluent in Python and modern ML stack such as PyTorch or JAX, and distributed training frameworks.
Creative approach to bringing definition and solutions to under-specificed challenges.
Are excited by fundamental questions around how intelligent systems acquire, retain, and use knowledge over time.
Thrive in fast-moving, collaborative environments, and communicate technical matters with clarity.
Take a high-ownership, “make it work” approach to problem solving.
Publications in top-tier venues.
Location:
Onsite in San Mateo, CA. Relocation support available.
Benefits:
Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.
Interview process:
Initial screening call (30 mins)
Biographical/behavioural interview (60 mins)
Technical interview (75 mins)
CEO interview (30 mins)
TL;DR: If you:
Have yourself designed complex physical systems;
Can translate customer workflows into a coherent, scalable product;
Have a rigorous understanding of AI models, agents, evals, and failure modes;
Have taken an AI product from early capability to sustained use;
Are as happy and capable being hands-on, as you are building and leading a team;
Share our vision of an AI engineer for the physical world…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI.
About the opportunity:
This is one of the most impactful roles in the company. You will work alongside the CEO to refine and translate the product vision for Archie into an actionable roadmap and strike the delicate balance between vision-driven and customer-driven product development. You must bring to the role a thorough understanding of how complex physical products are designed today, as well as a nuanced perspective on the capabilities and limitations of modern AI models and harnesses. You will be a key driver of our product strategy and execution, and as such you must be a true believer in our mission to build an engineering ASI for the physical world.
About the role:
Continuously map our ambitious vision into a focused product strategy and feature roadmap.
Embed with customers, understand their workflows, and inject customer use cases into the product roadmap.
Develop feature specifications and requirements alongside the engineering teams.
Work alongside engineering leaders to drive overall product architecture.
Lead product from emerging AI capability to trusted, sustained use in real engineering environments, setting a rigorous bar for performance, evals, reliability, and graceful failure modes.
Build and lead a world-class product team—while staying deeply hands-on with discovery, design, and delivery.
About you:
You have intimate familiarity with engineering design for some class of complex systems. If the complex systems align with our target product verticals: data center cooling and power systems, automotive, aerospace and defense—so much the better.
You are deeply embedded in the AI ecosystem and have a thorough understanding of how modern AI works, its limitations, and its failure modes.
You’ve taken an AI product from early capability to sustained customer adoption.
You love the 0->1 part of the journey, being hands on, attracting top talent, and motivating a team.
You share our conviction that AI building the physical world will be beneficial and transformative for the human experience.
Location:
Remote (US/Canada) or in-office in San Mateo, CA. Remote employees spend one week out of six working together on-site in our San Mateo office. Relocation support available.
Benefits:
Competitive salary, bonus, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.
Interview process:
Introductory call (30 mins)
Biographical/behavioural interview (45 mins)
Case study interview (60 mins)
CEO interview (30 mins)
TL;DR: If you:
have mastered extreme systems in a physical engineering domain to know what peak engineering actually looks like;
have built or worked with AI evaluations, including judges and human evaluation;
can work comfortably with customers, subject matter experts, partners, and technical teams;
are motivated by the goal of building superintelligence for engineering…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI.
About the opportunity:
In this role, you’ll sit at the forefront of AI for physical engineering, working with subject matter experts, customers, partners, and our internal teams to challenge and evaluate Archie on tough engineering work, understand where it fails, and capture those failures in evaluations that guide product development. You need to understand not only whether outlook is plausible, but whether the underlying engineering reasoning and decisions make sense.
About the role:
Study how engineers and customers use Archie in the field, identify where Archie succeeds or fails from an engineering perspective, and turn those observations into actionable evaluations.
Recreate real-world engineering tasks and failure modes in environments that can be repeatedly used by our development teams.
Build and refine evaluation methodologies for Archie, including automated judges and human evaluation processes.
Assess whether automated judges actually align with expert human judgment and improve them when they do not.
Work across engineering, product, customers, SMEs, and external partners to make sure our evaluations reflect real engineering workflows rather than abstract benchmarks.
Use evaluation results to help the team understand where Archie needs to improve and which problems matter most to users.
About you:
Technical background in a physical engineering domain such as power systems, power electronics, mechanical engineering, aerospace, robotics, or a closely related field.
Strong enough understanding of agentic AI systems to design and reason about evaluations.
Experience with AI evaluations, including creating evaluation tasks, designing or crafting judges, and comparing automated evaluation against human judgment.
Comfortable working directly with customers, engineers, subject matter experts, partners, and internal technical teams to extract what matters from ambiguous workflows.
Strong product judgment, with the ability to turn observations about how a system is being used or failing into concrete evaluation work.
Motivated by the mission of building superintelligence for engineering and excited by the challenge of making AI systems genuinely capable in the physical world.
Location:
Remote (US/Canada) or San Mateo, CA. Remote employees spend one week out of six working together on-site in our San Mateo office. Relocation support available.
Benefits:
Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.
Interview process:
Introductory call (30 mins)
Biographical/behavioural interview (45 mins)
Technical interview (60 mins)
CEO interview (30 mins)
TL;DR: If you:
Love to sell, especially AI products;
Have built a sales pipeline 0->1;
Have experience selling agentic AI or complex enterprise software;
Have sold into heavy industrials: data centers, automotive, aerospace;
Thrive in rapidly-changing, semi-ambiguous, high-agency environments…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training datasets and environments that create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included several AI luminaries from Anthropic and Nominal alongside our existing angel investors from Google and OpenAI.
About the opportunity:
You’ll be our first AE hire, so you’ll have significant agency to shape the role and our go-to-market strategy. You'll be responsible for identifying and winning new enterprise customers, developing relationships with some of the world's most complex industrial organizations, and helping establish the commercial playbook for an entirely new category of AI technology.
About the role:
Own net-new customer acquisition end-to-end,from identifying a strategic account and finding a way in, through discovery, technical validation, negotiation, contracting, and close.
Create a pipeline through thoughtful outbound, your existing network, events, referrals, and whatever else works to get P-1 AI in front of the right companies.
Work hand in hand with our Forward Deployed Engineers and Deployment Managers to validate scope during discovery and hand off closed customers with clear success criteria.
Shape the sales playbook for a new category, including the messaging, collateral and events that drive pipeline.
About you:
Experience winning complex enterprise business, including meaningful time in an early-stage start-up environment.
A track record of creating enterprise pipeline from a standing start. You can point to important accounts that you opened through your own outbound, relationships, creativity, or persistence.
Experience selling AI, engineering software, or similarly technical technology into industrial customers, with enough technical curiosity to earn credibility with engineers as well as senior business leaders.
A strong network across industrial, engineering, or technology organizations that can open doors.
Excited by owning the outcome, not staying inside a narrowly defined sales job description.
Location:
Remote (US/Canada) or San Mateo, CA. Remote employees spend one week out of six working together on-site in our San Mateo office. Relocation support available.
Benefits:
Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.
Interview process:
Initial screening call (30 mins)
Biographical/behavioural interview (45 mins)
Sales interview (60 mins)
CEO interview (30 mins)
TL;DR: If you:
can rapidly prototype, integrate, and ship AI systems into production;
love turning messy real-world problems into elegant technical solutions;
have built something remarkable, at work or outside of it…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI.
About the opportunity:
Our Forward Deployed Engineers serve as the technical lead for each customer deployment, making key technical decisions on architecture, data integrations, and user experience for specific customer missions. On a daily basis, you will build end-to-end AI solutions, take them into production, and support our customers solving real-world physical engineering problems with novel AI capabilities.
About the role:
Work with customers over the entire Archie product lifecycle: Lead discovery sessions, translate engineering challenges into AI use cases, design how Archie integrates into each workflow, and provide post-deployment support to ensure customer success.
Design, implement, and refine customer integrations: Connect Archie to engineering data sources and tools, iterate until it delivers measurable value.
Fine-tune and evaluate AI systems in production environments: Identify, collect, and prepare customer data to fine-tune models. Source evaluation criteria and questions from customer sessions to validate improved engineering capability.
Collaborate with engineering teams: Work closely with subject matter experts and AI specialists to develop novel AI systems capable of executing critical customer engineering tasks.
Diagnose and resolve critical issues in live deployments: Drive root-cause analysis and implement durable improvements across the organization.
About you:
Experience shipping data-driven or AI systems to production (Python experience strongly preferred).
A physical engineering background (automotive, aerospace, electrical, mechanical, etc.).
Can rapidly build integrations between disparate customer systems.
Comfortable working directly with customers and technical leaders.
Have integrated or extended AI systems. Bonus points for engineering or scientific domains!
Delivered solutions in secure enterprise environments.
Handled live production troubleshooting and customer incident response.
Location:
Remote (Australia) with 40% of time spent on customer site in Perth.
Interview process:
Introductory call (30 mins)
Biographical/behavioural interview (45 mins)
Technical interview (60 mins)
CEO interview (30 mins)
TL;DR: If you:
know infrastructure inside out: Kubernetes, multi-cloud, distributed systems, networking, cybersecurity, and BYOC;
thrive in startups, move fast, and have a relentless drive to execute;
are deeply aligned with our mission to build AI for the physical world;
have a principled but aggressive approach to AI-assisted development…
… you should apply for this role!
About P-1 AI:
At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI.
About the opportunity:
As our dedicated Platform Engineer, you'll own the infrastructure that makes it possible for Archie to operate reliably inside customer environments, evolving our BYOC deployment architecture. You'll tackle hard problems spanning distributed systems, cloud infrastructure, and security, with significant ownership over how Archie is deployed and scaled.
About the role:
Own and evolve the BYOC deployment model for Archie across customer environments, including how systems are distributed, installed, and updated, and how they interact with P-1’s central systems.
Build and own Kubernetes-based infrastructure that runs reliably across multiple clouds and customer setups.
Create deployment tooling using Helm, GitOps, or similar approaches to make installation and operations repeatable.
Implement secure and reliable update and upgrade mechanisms that work even in restricted or locked-down network environments.
Work closely with product, research, and forward-deployed engineers to translate requirements into platform capabilities.
Debug and improve production deployments, identifying bottlenecks in performance, reliability, or operability.
Establish observability, monitoring, and debugging patterns that give us visibility into distributed systems we don’t fully control.
About you:
Have built and operated Kubernetes-based systems in production.
Have designed infrastructure using IaC tools like Terraform, Pulumi, or similar.
Have implemented CI/CD or GitOps workflows that deploy systems across environments.
You are comfortable working across cloud providers (AWS, GCP, Azure) and understand their core primitives.
You have worked on systems that require strong security and networking considerations (e.g., VPCs, identity, encryption).
You like owning problems end-to-end and are comfortable operating in ambiguous, 0-1 environments.
You’re constantly evolving your AI approach for your own technical work.
Location:
Remote (US/Canada) or San Mateo, CA. Remote employees spend one week out of six working together on-site in our San Mateo office. Relocation support available.
Benefits:
Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.
Interview process:
Introductory call (30 mins)
Biographical/behavioural interview (45 mins)
Technical interview (60 mins)
CEO interview (30 mins)