Harness Engineer

Nomic, Inc.; New York (NY); On-site; USD 100,000 - 130,000; Full time

Job summary

Nomic, Inc. is seeking a Harness Engineer in NYC to develop systems that enhance AI agents' effectiveness in managing complex real-world documents. This role involves architecting retrieval systems, assembling context, and ensuring system evaluation for agent accuracy.

The ideal candidate has strong skills in Python or TypeScript, and real-world experience with retrieval systems. Familiarity with LLMs and an interest in agent frameworks is a plus. Join us in shaping solutions for the AEC industry.

Qualifications

• Strong engineering skills in Python and/or TypeScript.
• Real experience with retrieval systems like embeddings or vector search.
• Proven work with messy, real-world data.

Responsibilities

• Build retrieval systems for search and ranking.
• Assemble context for agents from large document sets.
• Create infrastructure for measuring agent accuracy.

Skills

Strong software engineering skills in Python and/or TypeScript

Experience with retrieval systems

Experience building systems with messy, real-world data

Familiarity with LLMs and agent frameworks

Intellectual curiosity about retrieval and agent tooling

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 search, traditional IR, or some combination
• You've built systems that had to work on messy, real-world data — not just clean benchmarks
• Familiarity with LLMs and agent frameworks in practice, not just in theory
• You think in systems — how components interact, where things break, what doesn't scale
• Intellectual curiosity about the retrieval and agent tooling landscape as it exists right now

Even better if you have:

• Experience with evaluation infrastructure — evals, benchmarks, regression testing for AI systems
• Background in search, NLP, or information retrieval
• Exposure to the AEC industry or other document-heavy domains