Agentic AI Engineer - Core Product

MIND, our agentic platform, powers autonomous agents that design experiments, optimize manufacturing steps, and accelerate scientific discovery. This team has no shortage of ideas - what this seat owns is making them survive production. You’ll design the agent harness and evaluation infrastructure from first principles, operate it at scale, and still be running it a year from now. Releases here are human-gated by design: you’ll build the machinery that makes an agent’s work trustworthy enough for a process engineer to sign off on. You’ll partner with our security architecture lead on RBAC, audit trails, and network-isolated serving for air-gapped customer environments - that’s a dedicated seat, not this one., * Multi-agent orchestration on LangGraph - sequential, loop, parallel, and supervisor patterns chosen deliberately, deterministic and observable, built to survive real production conditions

• The operational backbone of long-running agents: workers, queues, timeouts, retries, checkpointing, backpressure, and failure domains
• Evaluation infrastructure as a first-class system - LLM-as-judge gating, golden datasets, replay runs, and full-stack tracing
• Human-in-the-loop machinery: checkpoints, review tooling, and judge-gated flows that decide what reaches a person
• MCP servers and tool-registration APIs that give agents persistent context and shared state across complex workflows
• Knowledge-graph-backed agent context (Neo4j) and RAG pipelines tuned for materials-science and fab retrieval
• The API contracts with the core Multiscale platform - the foundation everything else depends on, * You’ve gated something real on evaluation - judge thresholds, golden-set regressions, replay runs - and evals show up in your answers before we ask

• 5+ years of production software engineering, including 2+ years shipping LLM or agentic systems; Docker and Kubernetes are second nature on Azure, AWS, or GCP, * Programmatic prompt optimization (DSPy or similar)
• Uncertainty quantification or model calibration work
• Graph databases in production
• Network-isolated or regulated-environment deployment experience
• Any exposure to semiconductor manufacturing, fab operations, or materials science
• Open-source agentic work - show us the system you operated longest, not the flashiest demo

• Own subsystems end to end and run them release after release - real ownership, not handoffs
• About 30 people, fast decisions, many hats, and direct access to customers and their hardest problems
• Work at the intersection of frontier AI and one of the world’s most complex engineering domains
• Competitive compensation with meaningful equity