Staff ML Research Scientist

Remote: yes Posted: 2025-10-16

- 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.

- 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.