Nomic- Harness Engineer

openreqstaffing; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

Nomic is seeking a Harness Engineer to build the systems that power our AI agents. You will design retrieval, context assembly, and evaluation infrastructure to ensure agents operate reliably across massive document collections.

Join us to shape how information is found, assembled, and evaluated, improving agent performance and scalability in real-world environments as we expand in 2026.

Qualifications

• Strong software engineering skills in Python and/or TypeScript.
• Experience with retrieval systems, embeddings and vector search.
• Built production systems that handle messy real-world data.
• Familiarity with LLMs and agent frameworks in practice.

Responsibilities

• Design and implement retrieval systems for large document collections.
• Engineer context assembly and orchestration for agents.
• Build evaluation infrastructure to measure accuracy and regressions.
• Develop scalable agent pipelines across multi-customer datasets.

Skills

Python

TypeScript

Retrieval systems

Embeddings

Vector search

LLM frameworks

Systems thinking

Problem solving

Debugging at scale

Tools

Vector databases

Embeddings tooling

LLM frameworks

Job description

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