Member of Technical Staff, Applied AI

Role overview Own the applied machine-learning systems that improve a diagnostic platform as it handles more investigations. The focus is production engineering: converting investigations and human corrections into structured data, measuring diagnostic quality, adapting models, and serving them efficiently. The work is centered on deployed systems rather than training foundation models from scratch or pursuing research publication.

Responsibilities - Instrument product workflows so investigations, especially human-corrected ones, become useful structured training data. - Build evaluation systems that measure diagnostic accuracy and root-cause correctness, including trajectory-level evaluation for agentic workflows. - Fine-tune and distill open-weight models so smaller models can handle suitable tasks at lower inference cost. - Deploy and scale inference on managed platforms, and support on-premises or air-gapped deployments for customers who cannot export device logs. - Work with product and engineering colleagues to connect model quality, operational constraints, and real diagnostic use cases.

Requirements - At least 10 years of relevant engineering experience. - End-to-end experience building production ML systems, including data pipelines, fine-tuning, evaluation, and deployment. - Hands-on fine-tuning and distillation of open-weight language models. - Experience designing evaluations for correctness-sensitive systems, including agent or tool-use trajectories. - Experience deploying and scaling inference with vLLM or an equivalent serving approach. - Proficiency with command-line AI coding tools in day-to-day work.

Nice to have - Agent and tool-use systems, preference optimization, or structured-output and function-calling fine-tuning. - On-premises or air-gapped model serving, retrieval across large and varied corpora, or systems and infrastructure experience.

Benefits and work setup - Remote work with at least four hours of daily overlap with Pacific or India Standard Time; occasional in-person meetings may be possible near Seattle or Bengaluru. - Top-of-market salary, founding equity, comprehensive medical insurance for the employee and family, and flexible time off. - Early ownership and visibility into product decisions, with access to AI tools and a small team that works closely together.