Member of Technical Staff

Responsibilities Evals as a first-class system. Turn production traces into golden datasets, automated graders, and regression tests. Every change ships with a number. Agentic harnesses and environments: the tooling that runs agents reproducibly for evaluation, data generation, and RL. Post-training infrastructure. Data pipelines, SFT and RL runs, and the plumbing that takes a specialized model from experiment to deployment. Reward signals and verifiers. Make fuzzy notions of quality measurable and hill-climbable. Latency and cost. Model routing, caching, parallel tool calls, and serving specialized models economically alongside frontier ones. About you 3+ years building production AI systems. You've shipped real systems to real users. Numbers over opinions. You've built eval harnesses from scratch and know the difference between a metric that hill-climbs and a metric that lies. Model-agnostic. You reason about prompt vs. context vs. model changes and know when each one wins. High agency. You find the highest-leverage problem and ship the fix without waiting for a spec. AI-native. You use Claude, Cursor, and Codex daily and have earned opinions on where they shine and where they mislead. Bonus Hands-on with post-training: SFT, DPO, RLHF/RLAIF, LoRA. Built RL environments, verifier models, or synthetic data pipelines. Inference-level work: speculative decoding, KV cache, batching. Published research, OSS, or technical writing on agents, evals, or inference. 0-to-meaningful-scale experience with an AI product. #J-18808-Ljbffr