Applied AI Engineer

About the Role As an Applied AI Engineer, you'll work on everything from browser agent reliability to document understanding to inference optimization, building systems that make the work more accurate and faster every week. This is a role for someone who thrives on turning research into production, working on problems like pushing to state‑of‑the‑art across core automation capabilities (UI interaction, unstructured data parsing, tool use), building adaptive systems that self‑heal when environments change, designing fine‑tuning pipelines that learn from customer‑specific workflows, and optimizing latency across the stack through model selection, quantization, caching, and routing strategies. Requirements (Must-Have) Strong Python and ML frameworks, particularly PyTorch. Eval-and-metric mindset. You think in terms of metrics that matter in production, not just benchmarks. Comfort with messy data and figuring out how to make it useful. Track record of shipping. You can describe specific systems you've built end‑to‑end. Crisp communication about your own work. You can describe what you built in a few clear sentences without buzzwords. Based in San Francisco or willing to relocate, in‑person 5 days a week. Nice‑to‑Have Experience with RL, retrieval systems, or agent‑based systems. Cross‑stack range: inference optimization, data pipelines, fine‑tuning, and model monitoring. Published ML papers or significant OSS contributions. Lab or research exposure (SAIL, BAIR, MIT CSAIL, similar). Recent applied work on LLMs, browser agents, RAG, or production AI workflows. #J-18808-Ljbffr