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
Build robust evaluation pipelines for the agent: offline evals (golden tasks, regression suites, behavior tests), online metrics (latency, success rate, fallout modes, cost efficiency), and experimentation frameworks (A/B, canaries, guardrail thresholds)
Productionize applied LLM techniques: function/tool-calling orchestration, self-reflection, retrieval/RAG, multi-agent handoffs, caching/embedding strategies, and hallucination reduction
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
Pragmatic experimentation: hypothesis → prototype → measured improvement → rollout
Excellent debugging and instrumentation skills; you enjoy finding and fixing edge cases in the wild
Nice To Have
Experience with agent frameworks, tool orchestration, and memory architectures
RAG systems in production (chunking, retrieval quality, freshness strategies)
Redis, Postgres/Supabase, queues (e.g., Celery/Arq/SQS), and event-driven designs
Observability stacks (Datadog, OpenTelemetry), and cost/latency optimization
Why Join Us
Mission: build autonomous agents that run entire businesses
Impact: ship core agent improvements that users feel immediately
Velocity: small, senior team; fast decision cycles; high ownership
Stack: modern tooling across AI orchestration, integrations, and memory systems
Compensation
Competitive salary and meaningful equity
Comprehensive benefits and flexible work setup