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About the Role
Our agents reason over massive, messy, real-world document collections — construction drawings, specifications, decades of project history. 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.
Responsibilities
- Develop retrieval systems including search, ranking, chunking strategies, and hybrid approaches.
- Engineer context assembly for agents operating over large, heterogeneous document sets.
- Build evaluation and harness infrastructure to measure agent accuracy, regression-test retrieval quality, and close feedback loops.
- Develop agent pipelines for orchestration between retrieval, models, and downstream actions.
- Ensure systems scale across thousands of customer document collections.
Requirements
- Strong software engineering skills in Python and/or TypeScript.
- Real experience with retrieval systems (embeddings, vector search, traditional IR, or a combination).
- Experience building systems that work on messy, real-world data.
- Familiarity with LLMs and agent frameworks in practice.
- Ability to think in systems, understanding component interactions, failure points, and scalability.
- Intellectual curiosity about the retrieval and agent tooling landscape.
Skills
- Python
- TypeScript
- Retrieval systems
- Embeddings
- Vector search
- Traditional IR
- LLMs
- Agent frameworks
- System design
- Evaluation infrastructure
- Search
- NLP
- Information retrieval
About the Company
- 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.