Senior AI Engineer — LLM Agents

What you will do

Design, build, and productionize agentic systems (multi-step reasoning, tool orchestration, guardrails) for materials science search, Q&A and information extraction.

Develop, integrate and maintain memory systems, MCP servers and agent skills in a multi-agent environment.

Build evaluation frameworks with domain experts to measure answer quality, extraction accuracy, and retrieval performance.

Own production reliability & observability of agents you develop.

Advise adjacent teams on agentic and search system design; flag technical risk and feasibility during roadmap planning.

Requirements

Degree in engineering, computer science, or a quantitative/physical science — or equivalent practical experience.

5+ years of software/ML engineering, including 2+ years building LLM-based systems that run in production.

You have designed evaluations for LLM/agent systems — eval sets, quality metrics, human-expert or LLM-judge pipelines — and can walk us through one (e.g., promptfoo, Braintrust, LangSmith, DeepEval, or your own harness).

You have instrumented, monitored, and debugged live AI services (e.g., OpenTelemetry, Arize Phoenix, Langfuse, Datadog, or similar).

Strong Python; able to independently build and deploy services.

Strong pluses (not required — you'll have room and support to pick these up on the job)

Search/RAG: vector databases, keyword search, knowledge graphs, reranking, hybrid retrieval

MCP (Model Context Protocol) or agent-tool ecosystem experience

Materials science, chemistry, or patent/IP domain exposure

Structured information extraction from technical documents (tables, compositions, specs)

Why this role

Full ownership of a production agent stack that customers pay for

Your evals help decide the roadmap: we build where we can measurably beat frontier general agents

Small senior team, direct access to domain experts and real R&D users