Applied AI Engineer - Agent

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

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