4+ years backend engineering experience, preferably in Python with an impact-focused mindset
Hands-on experience with LLMs: adept in prompt engineering, function-calling, retrieval, and evaluation design
Proven ability to build evaluation harnesses for driving performance improvements
Strong foundation in distributed systems: concurrency, reliability, and data management
A pragmatic approach to experimentation, from hypothesis to rollout
Skilled in debugging and instrumentation, with a knack for addressing edge cases
Responsibilities
Design and implement end-to-end agent improvements, including safety protocols and memory management
Build and maintain robust evaluation pipelines, optimizing both offline and online metrics
Productionize LLM techniques, managing function orchestration and response handling
Enhance core backend systems for reliability and scalability, integrating with various platforms
Collaborate with product and infrastructure teams to define metrics and accelerate deployment of solutions
Write clean, effective code and document all design decisions and processes
Benefits
Mission-driven work focused on creating autonomous agents for businesses
Immediate impact on core agent functionalities users experience
Be part of a small, agile, and senior team with quick decision-making
Work with cutting-edge technology in AI orchestration and memory systems
Full Job Description
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
