We’re hiring an Applied AI Engineer to push the boundaries of our Cofounder agent. You’ll own core backend systems and applied LLM work: advancing agent reliability and autonomy, building evaluation pipelines, and shipping techniques that measurably improve agent performance. This is a hands‑on role with high ownership across research‑to‑production: prototyping, instrumenting, evaluating, and deploying improvements that show up directly in user outcomes.
What You’ll Do
Design and implement agent improvements end‑to‑end: prompting strategies, tool selection, action planning, memory usage, safety/guardrails, and recovery paths
Improve core backend systems: reliable job orchestration, retries/backoff, idempotency, and auditability; scalable memory and context routing; data pipelines across Gmail, Slack, Notion, Linear, Google Workspace, etc.; observability and tracing for agent actions/outcomes
Partner with product and infra to define success metrics and ship fast, safe iterations
Write clean, well‑tested code; document design decisions and runbooks
What You’ll Bring
4+ years backend engineering experience, preferably Python (we care about impact over years)
Hands‑on LLM experience: prompt engineering, function‑calling, retrieval, embeddings, evaluation design; you’ve shipped LLM features to production
Track record building evaluation harnesses and using them to drive improvements (regression suites, task success metrics, cost/runtime tradeoffs)
Solid distributed systems fundamentals: concurrency, reliability, performance, data modeling, lifecycle management