Staff ML Research Scientist

What You'll Do

• Own end-to-end applied research: frame the problem, design experiments, ship to production, and monitor impact against real-world metrics.
• Set technical direction across LLMs, retrieval, and multimodal; run ablations/error analysis that change product decisions.
• Build evaluation that matters: link offline metrics to online outcomes; define thresholds, monitoring, and rollback.
• Partner to deliver with engineering and product—and, when relevant, clinicians/domain experts—to align data, success criteria, and timelines.
• Raise the bar by mentoring peers and codifying standards for reliability, safety, and documentation.
• Improve the platform (data, training, serving, observability) to speed iteration and ensure reproducibility.
• Explore new directions, with computer vision/vision-language work as a nice-to-have for future strategic initiatives.

What We're Looking For

• MS or PhD (or equivalent research experience) in Computer Science, Electrical Engineering, Computational Linguistics, Biomedical Informatics, or related quantitative field.
• 7+ years of applied ML research experience (or PhD + 5 years, or equivalent evidence of Staff-level impact).
• Depth in one or more areas: LLMs and NLP, computer vision, speech, recommendation/ranking, retrieval, or multimodal modeling.
• Strong experimental rigor: clear hypothesis framing, offline→online linkage, calibration and stratified analyses, ablations that influence decisions.
• Proven ability to take models to production.
• Hands-on with modern tooling: PyTorch and common experiment/ops tools (for example MLflow, Databricks, Ray, or similar).
• System thinking: can choose methods based on constraints, design for observability and rollback, and document decisions clearly.
• Collaborative communicator who writes crisp design docs and explains complex ideas to non-specialists; comfortable mentoring peers.

Preferred Qualifications

• Health data familiarity, including EHR or imaging.
• Experience in one or more areas: clinical NLP or LLMs, computer vision, speech, retrieval or multimodal modeling.
• Shipped, measured models in production with monitoring and clear rollback; external or multi-site validation is a plus.
• Workflow integration with EHR, RIS, PACS, or reporting systems; PowerScribe or Dragon exposure helpful.
• Strong evaluation practices: calibration, slice analysis, and ablations.
• Safety and governance in sensitive domains, including PHI handling and HIPAA or FDA-adjacent environments.
• Technical mentorship and contributions to team research culture; publications or impactful open-source work.
• Practical tooling: PyTorch plus modern ML ops tools such as MLflow, Databricks, Ray, or Triton.