Nomic
AI/ML, Developer Tools, Architecture, Engineering, Construction
HarnessEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Harness Engineer at Nomic. Skills: Retrieval systems, Context engineering, Evaluation infrastructure, Agent pipelines. Solve retrieval problems. Solve context assembly problems”
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
Software engineering skills in Python, Software engineering skills in TypeScript, Real experience with retrieval systems, Built systems on messy data, Familiarity with LLMs and agent frameworks, Think in systems
Nice to Have
Experience with evaluation infrastructure, Background in search, Background in NLP, Background in information retrieval, Exposure to AEC industry, Exposure to document-heavy domains
What You'll Do.
Solve retrieval problems
Solve context assembly problems
Solve evaluation problems
Develop retrieval systems
Develop context engineering
Build evaluation infrastructure
Measure agent accuracy
Regression-test retrieval quality
Develop agent pipelines
Full Job Description
HARNESS ENGINEER Location: NYC Reports to: CTO ABOUT NOMIC Nomic builds AI agents and developer tools that power the built world. We help enterprise teams in architecture, engineering, and construction extract structured knowledge from decades of drawings, specs, and project files. Our platform combines embedding models, document parsing, and autonomous agents that reason over real-world data and take action in live environments. THE ROLE Our agents reason over massive, messy, real-world document collections — construction drawings, specifications, decades of project history. Getting that right means solving retrieval, context assembly, and evaluation as first-class engineering problems, not afterthoughts bolted onto a prompt. We're hiring a Harness Engineer to work on the systems that make our agents effective: how they find information, how they assemble context, how we know they're working, and how we make them better over time. You should be the kind of engineer who knows what a vector database is and when not to use one. Who thinks about retrieval as an architecture problem, not a library call. Who's paying attention to how agent systems actually get built and deployed in 2026 — and has opinions about it. WHAT YOU'LL WORK ON - Retrieval systems — search, ranking, chunking strategies, hybrid approaches, knowing which tool fits which problem - Context engineering — assembling the right information for agents operating over large, heterogeneous document sets - Evaluation and harnesses — building the infrastructure to continuously measure agent accuracy, regression-test retrieval quality, and close feedback loops - Agent pipelines — the orchestration layer between retrieval, models, and downstream actions - Scale — making all of the above work across thousands of customer document collections, not just a demo corpus WHAT WE'RE LOOKING FOR - Strong software engineering skills in Python and/or TypeScript - Real experience with retrieval systems — embeddings, vector s
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