Join us and help shape the future of AI by defining the narrative around document understanding.
Don’t see a role that feels like the right fit? We’d still love to hear from you. Submit your application here, and we’ll keep you in mind as new opportunities open up!
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
","descriptionPlain":"Join us and help shape the future of AI by defining the narrative around document understanding.\n\n\n\nDon’t see a role that feels like the right fit? We’d still love to hear from you. Submit your application here, and we’ll keep you in mind as new opportunities open up! \n\n\n\n\n\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.\n\n\n\nLlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes."},{"id":"5ef32675-090d-4eb2-bca0-ddb6fbde0215","title":"Member of Technical Staff, Applied Research","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco","secondaryLocations":[],"publishedAt":"2026-07-08T01:11:47.282+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/llamaindex/5ef32675-090d-4eb2-bca0-ddb6fbde0215","applyUrl":"https://jobs.ashbyhq.com/llamaindex/5ef32675-090d-4eb2-bca0-ddb6fbde0215/application","descriptionHtml":"Join us and help shape the future of AI by defining the narrative around document understanding.
We are looking for an AI Research Engineer to join our document understanding team.
This role is ideal for someone who sits between applied research and strong engineering. You will work on vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training. The goal is simple: make our document AI systems more accurate, faster, and more cost-effective in production.
You should be excited by frontier AI work, but equally motivated by practical product impact. This is not a pure research role where ideas stay in papers. You will be expected to prototype quickly, evaluate rigorously, and help turn promising approaches into production systems used by customers.
Develop and train vision-language models for document processing and document understanding.
Build data pipelines for data curation, synthetic data generation, labeling, and benchmark creation.
Evaluate base models and perform post-training or fine-tuning to hit specific performance targets.
Improve model accuracy, latency, and cost-effectiveness across real-world document workflows.
Design and maintain benchmarks to measure extraction quality, layout understanding, OCR performance, reasoning accuracy, and end-to-end system reliability.
Work with messy real-world documents, including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents.
Collaborate with engineering to move successful research prototypes into production.
Work directly with customers when needed to translate product requirements into benchmarks, experiments, and model improvements.
Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems.
Use modern AI coding workflows and tools to move quickly.
3–7 years of experience in machine learning engineering, applied research, or research engineering.
Strong ML foundation, including hands-on experience benchmarking and training models.
Strong Python skills and comfort with modern ML tooling, especially PyTorch.
Experience with computer vision, vision-language models, NLP, document AI, OCR, extraction, or agentic AI systems.
Ability to build experiments, evaluate results, and iterate quickly toward measurable performance improvements.
Strong engineering judgment and ability to write clean, production-quality code.
Comfort working in a fast-paced startup environment with high ownership and limited structure.
Adaptable, scrappy, and self-directed — someone who can figure things out without waiting to be told.
Strong technical writing and communication skills.
Prior startup experience, especially at an early-stage or high-growth AI company.
Experience as a founder or early startup engineer.
Experience building or improving document processing systems.
Experience with synthetic data generation, post-training, fine-tuning, or benchmark design.
Familiarity with tools such as vLLM, Pydantic, uv, ruff, mypy, Claude Code, Cursor, or similar modern AI engineering workflows.
Experience with open-source AI infrastructure or developer tools.
You will work closely with the CTO and the document understanding team, partnering across research, engineering, product, and customer-facing teams.
Work on a core AI infrastructure problem: making complex documents understandable and actionable for AI systems.
Build production systems at the frontier of vision-language models and document AI.
Join a fast-growing startup with strong open-source adoption and commercial traction.
Work directly with technical founders and a highly ambitious engineering team.
Have real ownership over model quality, product capability, and technical direction.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
","descriptionPlain":"Join us and help shape the future of AI by defining the narrative around document understanding.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for an AI Research Engineer to join our document understanding team.\n\nThis role is ideal for someone who sits between applied research and strong engineering. You will work on vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training. The goal is simple: make our document AI systems more accurate, faster, and more cost-effective in production.\n\nYou should be excited by frontier AI work, but equally motivated by practical product impact. This is not a pure research role where ideas stay in papers. You will be expected to prototype quickly, evaluate rigorously, and help turn promising approaches into production systems used by customers.\n\n\n\n\nWHAT YOU’LL DO\n\n - Develop and train vision-language models for document processing and document understanding.\n\n - Build data pipelines for data curation, synthetic data generation, labeling, and benchmark creation.\n\n - Evaluate base models and perform post-training or fine-tuning to hit specific performance targets.\n\n - Improve model accuracy, latency, and cost-effectiveness across real-world document workflows.\n\n - Design and maintain benchmarks to measure extraction quality, layout understanding, OCR performance, reasoning accuracy, and end-to-end system reliability.\n\n - Work with messy real-world documents, including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents.\n\n - Collaborate with engineering to move successful research prototypes into production.\n\n - Work directly with customers when needed to translate product requirements into benchmarks, experiments, and model improvements.\n\n - Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems.\n\n - Use modern AI coding workflows and tools to move quickly.\n\n\n\n\nWHAT WE’RE LOOKING FOR\n\n - 3–7 years of experience in machine learning engineering, applied research, or research engineering.\n\n - Strong ML foundation, including hands-on experience benchmarking and training models.\n\n - Strong Python skills and comfort with modern ML tooling, especially PyTorch.\n\n - Experience with computer vision, vision-language models, NLP, document AI, OCR, extraction, or agentic AI systems.\n\n - Ability to build experiments, evaluate results, and iterate quickly toward measurable performance improvements.\n\n - Strong engineering judgment and ability to write clean, production-quality code.\n\n - Comfort working in a fast-paced startup environment with high ownership and limited structure.\n\n - Adaptable, scrappy, and self-directed — someone who can figure things out without waiting to be told.\n\n - Strong technical writing and communication skills.\n\n\n\n\nNICE TO HAVE\n\n - Prior startup experience, especially at an early-stage or high-growth AI company.\n\n - Experience as a founder or early startup engineer.\n\n - Experience building or improving document processing systems.\n\n - Experience with synthetic data generation, post-training, fine-tuning, or benchmark design.\n\n - Familiarity with tools such as vLLM, Pydantic, uv, ruff, mypy, Claude Code, Cursor, or similar modern AI engineering workflows.\n\n - Experience with open-source AI infrastructure or developer tools.\n\n\n\n\nWHO YOU’LL WORK WITH\n\nYou will work closely with the CTO and the document understanding team, partnering across research, engineering, product, and customer-facing teams.\n\n\n\n\nWHY JOIN LLAMAINDEX\n\n - Work on a core AI infrastructure problem: making complex documents understandable and actionable for AI systems.\n\n - Build production systems at the frontier of vision-language models and document AI.\n\n - Join a fast-growing startup with strong open-source adoption and commercial traction.\n\n - Work directly with technical founders and a highly ambitious engineering team.\n\n - Have real ownership over model quality, product capability, and technical direction.\n\n\n\n\n\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.\n\n\n\nLlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes."},{"id":"0f8bdc04-5d1d-4db1-a888-3f367cb4469e","title":"Head of Engineering","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco","secondaryLocations":[],"publishedAt":"2026-10-08T23:26:16.411+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/llamaindex/0f8bdc04-5d1d-4db1-a888-3f367cb4469e","applyUrl":"https://jobs.ashbyhq.com/llamaindex/0f8bdc04-5d1d-4db1-a888-3f367cb4469e/application","descriptionHtml":"Join us and help shape the future of AI by defining the narrative around document understanding.
LlamaIndex makes enterprise data useful for AI applications.
You will own engineering execution, technical direction, hiring, and team development. You will work closely with company leadership and product teams to decide what we build, how we build it, and how we deliver it reliably to customers.
This is a hands-on role. You should be comfortable reviewing code and architecture, debugging production issues, and writing code for critical systems. We are looking for someone who has shipped and operated B2B API products and understands both model serving and model training—from experimentation and evaluation to deployment and production operations.
Lead and develop our engineering team, setting clear priorities, ownership, and accountability.
Partner with company leadership to translate customer needs into a focused roadmap and deliver software predictably.
Stay directly involved in architecture, code reviews, production debugging, and implementation of critical changes.
Set technical direction across application services, APIs, data pipelines, and ML infrastructure, balancing immediate customer needs with long-term maintainability.
Work with ML engineers and researchers on model training, fine-tuning, evaluation, and deployment, ensuring model improvements translate into better production outcomes.
Guide model serving decisions across latency, throughput, GPU utilization, capacity, reliability, and inference cost.
Improve engineering practices for testing, observability, security, incident response, and releases.
Hire strong engineers, coach technical leaders and managers, and build a culture of direct communication, customer focus, and responsibility for results.
Experience leading multiple engineering teams in B2B SaaS, at a scope comparable to a 20–30-person organization, with responsibility for delivering and operating customer-facing products.
Strong, current hands-on engineering skills: you can read and write production code, review architecture, and diagnose complex technical problems.
Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations.
Understanding of the tradeoffs between model quality, latency, throughput, GPU resources, reliability, and cost.
Strong background in backend systems, APIs, distributed systems, and cloud infrastructure.
A track record of hiring and developing engineers, setting clear expectations, and giving constructive feedback.
Good product judgment, including knowing when to move quickly and when to invest in correctness, reliability, and security.
Clear written and verbal communication with technical teams, customers, and company leadership.
Experience scaling engineering at a Series A–C startup.
Experience with document processing, OCR, extraction, retrieval, indexing, or other systems that work with enterprise data.
Experience with LLMs or multimodal models, evaluation datasets, and model quality monitoring.
Experience with GPU infrastructure, distributed training, inference optimization, or model serving frameworks.
Experience building developer tools or API-first products.
Experience with enterprise requirements such as multi-tenancy, access controls, auditability, and security reviews.
Experience developing engineering managers while staying closely involved in technical execution.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
","descriptionPlain":"Join us and help shape the future of AI by defining the narrative around document understanding.\n\n\n\n\nABOUT THE ROLE\n\nLlamaIndex makes enterprise data useful for AI applications.\n\nYou will own engineering execution, technical direction, hiring, and team development. You will work closely with company leadership and product teams to decide what we build, how we build it, and how we deliver it reliably to customers.\n\nThis is a hands-on role. You should be comfortable reviewing code and architecture, debugging production issues, and writing code for critical systems. We are looking for someone who has shipped and operated B2B API products and understands both model serving and model training—from experimentation and evaluation to deployment and production operations.\n\n\n\n\nRESPONSIBILITIES\n\n - Lead and develop our engineering team, setting clear priorities, ownership, and accountability.\n\n - Partner with company leadership to translate customer needs into a focused roadmap and deliver software predictably.\n\n - Stay directly involved in architecture, code reviews, production debugging, and implementation of critical changes.\n\n - Set technical direction across application services, APIs, data pipelines, and ML infrastructure, balancing immediate customer needs with long-term maintainability.\n\n - Work with ML engineers and researchers on model training, fine-tuning, evaluation, and deployment, ensuring model improvements translate into better production outcomes.\n\n - Guide model serving decisions across latency, throughput, GPU utilization, capacity, reliability, and inference cost.\n\n - Improve engineering practices for testing, observability, security, incident response, and releases.\n\n - Hire strong engineers, coach technical leaders and managers, and build a culture of direct communication, customer focus, and responsibility for results.\n\n\nREQUIRED QUALIFICATIONS\n\n - Experience leading multiple engineering teams in B2B SaaS, at a scope comparable to a 20–30-person organization, with responsibility for delivering and operating customer-facing products.\n\n - Strong, current hands-on engineering skills: you can read and write production code, review architecture, and diagnose complex technical problems.\n\n - Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations.\n\n - Understanding of the tradeoffs between model quality, latency, throughput, GPU resources, reliability, and cost.\n\n - Strong background in backend systems, APIs, distributed systems, and cloud infrastructure.\n\n - A track record of hiring and developing engineers, setting clear expectations, and giving constructive feedback.\n\n - Good product judgment, including knowing when to move quickly and when to invest in correctness, reliability, and security.\n\n - Clear written and verbal communication with technical teams, customers, and company leadership.\n\n\nPREFERRED QUALIFICATIONS\n\n - Experience scaling engineering at a Series A–C startup.\n\n - Experience with document processing, OCR, extraction, retrieval, indexing, or other systems that work with enterprise data.\n\n - Experience with LLMs or multimodal models, evaluation datasets, and model quality monitoring.\n\n - Experience with GPU infrastructure, distributed training, inference optimization, or model serving frameworks.\n\n - Experience building developer tools or API-first products.\n\n - Experience with enterprise requirements such as multi-tenancy, access controls, auditability, and security reviews.\n\n - Experience developing engineering managers while staying closely involved in technical execution.\n\n\n\n\n\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.\n\n\n\nLlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes."}],"apiVersion":"1"}