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Senior ML Research Scientist
Rad AI · San Francisco, California, United States
Pay
$170k–220k
Setting
Remote
ABOUT RAD AI
At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.
Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting-edge AI.
Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.
Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie https://www.radai.com/news/auntminnie-recognizes-rad-ai-omni-reporting-as-2023s-best-new-radiology-software, and ranked by Deloitte https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/fast500-winners.html as the 19th fastest-growing company in North America, we are building AI-powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 https://www.cnbc.com/2025/06/10/2025-cnbc-disruptor-50-see-the-full-list-of-companies.html list, highlighting the innovation and momentum behind our mission.
If you’re ready to shape the future of healthcare, we’d love to have you on our team!
WHAT YOU’LL DO
- Own a multimodal ML work-stream from problem definition through experimentation, evaluation, deployment, and iteration.
- Translate clinical and product needs into clear ML objectives, data strategies, model approaches, and success criteria.
- Build and evaluate modern ML systems, including transformers, self-supervised learning, weak supervision, detection, localization, and segmentation.
- Work with image, report, and other clinical data to develop systems that are useful in real radiology workflows.
- Design rigorous evaluations that go beyond aggregate offline metrics, including clinically meaningful operating points, robustness, calibration, and performance across relevant data slices.
- Partner with engineering to productionize models, make practical system tradeoffs, and learn from performance after launch.
- Investigate failure modes such as laterality errors, poor image or report grounding, hallucination, dataset bias, domain shift, and workflow disruption.
- Communicate research findings and technical decisions clearly through design documents, experiment reviews, and presentations to technical and clinical partners.
- Contribute to the research roadmap by identifying promising approaches, sharing learnings, and helping the team decide what to pursue next.
- Mentor less experienced researchers and engineers through project collaboration, code and experiment reviews, and technical guidance.
WHAT WE’RE LOOKING FOR
- Strong applied experience in computer vision, NLP, or deep learning, with a track record of independently designing experiments, analyzing results, and turning findings into working systems.
- Experience owning substantial ML projects across the full lifecycle, from data and modeling through production delivery.
- Deep hands-on ability in Python and PyTorch, with strong intuition for model architecture, data quality, experimentation, and evaluation.
- Experience with modern vision or multimodal techniques such as vision transformers, contrastive learning, masked image modeling, or weak supervision, etc.
- The judgment to connect model performance to real user and clinical outcomes, including knowing when a benchmark improvement is not enough.
- Strong collaboration skills across research, engineering, product, data, and clinical teams.
- Clear written and verbal communication, including the ability to explain technical tradeoffs to both ML experts and clinical partners.
- Typically 4+ years of relevant applied ML research or engineering experience, or equivalent scope and impact. We calibrate on demonstrated ownership rather than title or exact tenure.
- An MS, PhD, or equivalent practical experience in Computer Science, Electrical Engineering, Machine Learning, Biomedical Engineering, or a related quantitative field.
NICE TO HAVE
- Experience with medical imaging, radiology, healthcare, or another high-stakes application area.
- Familiarity with chest X-ray, CT, MRI, mammography, or other clinical imaging modalities.
- Experience with DICOM, image-report pairing, medical data de-identification, radiology workflows, or clinically derived labels.
- Experience evaluating models across patients, sites, scanner vendors, protocols, or other sources of distribution shift.
- Familiarity with clinical validation, FDA or HIPAA considerations, or other regulated and privacy-sensitive environments.
- Experience with 3D vision, longitudinal imaging, report generation, or clinical decision support.
- Publications, open-source contributions, or other evidence of research credibility.
WHAT SUCCESS LOOKS LIKE
You’ll own and advance a meaningful research track from ideation through production. You’ll establish a strong understanding of the clinical problem, build a credible data and evaluation strategy, deliver models that perform reliably in practice, and help the team learn from real-world use.
You’ll also become a trusted technical partner to the researchers, engineers, product leaders, data teams, and clinicians working on the broader ML roadmap. Over time, you’ll help raise the quality of research and technical decision-making through strong experimentation, clear communication, and thoughtful mentorship.
OUR WORKING STYLE
We’re a remote-first company with a highly collaborative, mission-driven research and engineering culture. We value direct communication, intellectual honesty, strong ownership, and practical judgment. The best work here comes from people who can go deep technically, stay close to the clinical context, and make progress even when the problem and the path are not fully defined.
This role is U.S. remote, with San Francisco Bay Area preferred. We encourage people from a wide range of backgrounds to apply. If the scope of this role excites you but your experience does not match every bullet, we would still love to hear from you.
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!
To learn more about what it's like to work at Rad AI, visit https://www.radai.com/life-at-rad-ai and be sure to follow us on LinkedIn https://www.linkedin.com/company/radai/?utm_campaign=Recruiting_2026&utm_content=recruiting-job-posting-website&utm_source=recru%5B%E2%80%A6%5Db-posting-website to stay up to date!
Location Details:
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.
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
At Rad AI, we value diversity and provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
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 http://radai.com or no-reply@ashbyhq.com.
Listed by Rad AI for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff ML Research Scientist
Rad AI · San Francisco, California, United States
Pay
$190k–260k
Setting
Remote
ABOUT RAD AI
At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.
Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting-edge AI.
Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.
Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie https://www.radai.com/news/auntminnie-recognizes-rad-ai-omni-reporting-as-2023s-best-new-radiology-software, and ranked by Deloitte https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/fast500-winners.html as the 19th fastest-growing company in North America, we are building AI-powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 https://www.cnbc.com/2025/06/10/2025-cnbc-disruptor-50-see-the-full-list-of-companies.html list, highlighting the innovation and momentum behind our mission.
If you’re ready to shape the future of healthcare, we’d love to have you on our team!
Why Join Us?
We're looking for a Staff Machine Learning Research Scientist to help define and drive Rad AI's next generation of applied research in NLP and clinical AI.
We work across LLMs, retrieval, representation learning, speech and multimodal modeling, and we care as much about evaluation and reliability as we do about state-of-the-art results. You will have scope, ownership, and a direct line from research to product.
You'll collaborate closely with clinicians, engineers, and product leaders to translate foundational research into production-scale systems that improve outcomes for doctors and patients alike. As we grow, you will help shape standards for model quality, safety, and observability, and contribute to strategic initiatives that include computer vision and vision-language work.
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.
We welcome applicants from across the United States, with a preference for this role to be based in our new San Francisco office.
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!
To learn more about what it's like to work at Rad AI, visit https://www.radai.com/life-at-rad-ai and be sure to follow us on LinkedIn https://www.linkedin.com/company/radai/?utm_campaign=Recruiting_2026&utm_content=recruiting-job-posting-website&utm_source=recru%5B%E2%80%A6%5Db-posting-website to stay up to date!
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
At Rad AI, we value diversity and provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
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 http://radai.com or no-reply@ashbyhq.com.
Listed by Rad AI for a position based in the United States. Employers on this board attest they are hiring domestically.
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