Own AI experiments end-to-end: shape hypotheses, define success criteria, build, ship, measure, and iterate
Partner with ML Engineering to productize ML prototypes and harden them for production
Design agent action schemas and tool contracts that make model behavior predictable under real-world variance
Build retrieval systems (RAG) for agent context: chunking/indexing strategies, vector search quality, and grounding behavior
Build orchestration systems for agent workflows: stateful sessions, streaming, async job coordination, and failure recovery
Build evaluation loops that combine deterministic validation, domain heuristics, and model-based review
Design human-in-the-loop flows for approvals, intervention, and safe execution of high-impact actions
Talk directly with users and internal stakeholders to refine problem framing and validate assumptions early

7+ years experience shipping production software, including customer-facing AI/LLM features
Ownership of ambiguous, high-impact problems from concept through deployment and iteration
Experience building AI agent systems: tool use, context management, and orchestration loops
Experience designing and operating RAG systems, including vector DB-backed retrieval pipelines
Experience defining or operating evaluation systems for non-deterministic AI outputs
Strong backend systems skills, including stateful services and real-time/streaming workflows
Experience managing AI costs in production (model selection, context budgeting, caching, and usage controls)
Experience collaborating across research, ML platform, and product teams to move models into production
Product engineering instincts: you can simplify scope and prioritize what creates measurable value
Clear written and verbal communication in a remote, async environment

comprehensive medical, dental, and vision coverage
unlimited PTO
meaningful maternity/paternity leave to all U.S based employees that are full-time
stipend to create the ideal home office

Senior AI Engineer, Labs

Key skills