Staff Machine Learning Engineer

Set technical strategy for a broad area of the ML roadmap, translating ambiguous business and research goals into scoped, production-ready systems.

Tackle the hardest modeling problems in the org - complex reasoning, long-context and multi-document understanding, or other frontier challenges as they come up.

Apply advanced ML techniques - fine-tuning, reinforcement learning, retrieval, or others - and know when a technique is the right tool versus over-engineering.

Establish rigorous evaluation standards, reducing hallucinations, improving factual consistency, and defining what "good" looks like for a given system.

Drive data excellence through hands-on analysis of training and evaluation data, managing noise, edge cases, and drift at scale.

Provide technical leadership and mentorship across the ML team, raising the bar for experimentation, benchmarking, and engineering rigor.

Act as the bridge between research and production - ensuring new techniques get integrated into shippable systems, not just proofs of concept.

Partner cross-functionally with product, engineering, and legal subject-matter experts to set technical direction.

Cost effectively scale practical machine learning systems in a hyper-growth environment, ensuring they remain grounded in real business and customer needs.

7+ years of hands-on ML engineering experience, with multiple models shipped and running in production.

Deep expertise in ML and NLP, including LLMs, with a track record of solving hard modeling problems - not just applying existing recipes.

High proficiency in Python and strong command of modern ML/NLP frameworks.

Demonstrated ability to set technical strategy and drive execution in ambiguous, fast-moving environments.

A track record of mentoring engineers and raising technical standards beyond your own output.