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.

• 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.

• 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.