# Staff ML Research Scientist

**Company:** [Rad AI](https://hotfix.jobs/companies/rad-ai)
**Location:** San Francisco, CA
**Role:** AI Research
**Salary:** $190k – $260k/yr
**Experience:** 7+ years
**Skills:** PyTorch, LLMs, NLP, Computer Vision, Retrieval, Multimodal Modeling, MLflow, Databricks, Ray, Triton
**Posted:** 2025-10-16

> Leads end-to-end applied ML research in NLP, LLMs, retrieval, and multimodal models for healthcare AI, driving from experimentation to production deployment with rigorous evaluation and clinician collaboration. Requires 7+ years experience, MS/PhD, and depth in ML areas like PyTorch tooling.

## Job Description

## 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**.

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