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.

Why This Matters

• Radiologists are the invisible backbone of modern medicine.
• Every diagnosis, every surgery, every treatment plan begins with their interpretations.
• Yet they're often overwhelmed by cognitive load, repetitive tasks, and administrative overhead.

At Rad AI, we're using ML to change that-building intelligent systems that understand medical context, streamline documentation, and amplify human expertise.

• You've already seen how AI can transform healthcare.
• Now help us push it further.
• Join us in shaping how AI supports the next generation of medical professionals.

• Join our world-class team as we build and deploy AI solutions that empower physicians and transform patient care-making a meaningful impact on millions of lives.
• Driven by our mission, we prioritize transparency, inclusion, and close collaboration, bringing together exceptional people to revolutionize healthcare.
• If you're passionate about driving innovation and delivering impactful healthcare solutions, we'd love to hear from you!

For roles listed as San Francisco - Onsite

• This role will be based in our San Francisco office and we expect employees to work onsite four days per week. The remaining time may be worked remotely or onsite, depending on team and business needs.

For roles listed as United States - Remote

• This role is open to candidates located anywhere in the United States.

For roles listed as San Francisco - Onsite + United States - Remote

• We will prioritize candidates who can work onsite four days per week in San Francisco, while also considering remote candidates located anywhere in the United States.

Benefits

For US-Based Full-Time Roles, Rad AI offers a variety of benefits, including:

• Comprehensive Medical, Dental, Vision & Life insurance
• HSA (with employer match), FSA, & DCFSA
• 401(k)
• 11 Paid Company Holidays
• Flexible PTO policy
• Annual company-wide offsite
• Periodic team offsites
• Annual equipment stipend
• For roles based outside the US, your recruiter can share more details

• Please be vigilant regarding job scams.
• We advise all candidates to apply directly through our official careers page.
• Our recruiters will use email addresses with the domain @radai.com or.