Senior ML Research Scientist

Rad AI•,

1d•$170,000 - $220,000•Remote

About The Position

Rad AI is a healthcare AI company on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology by saving time, reducing burnout, and improving patient care. We possess one of the largest proprietary radiology report datasets globally, and our AI has been instrumental in uncovering hundreds of new cancer diagnoses and reducing error rates in millions of radiology reports by nearly 50%. We have secured over $140M in funding, including a recent $68M Series C round led by Transformation Capital, valuing the company at $528M. Our investors include Khosla Ventures, World Innovation Lab, and Gradient Ventures. Our generative AI advancements are utilized daily by thousands of radiologists, supporting over one-third of radiology groups and healthcare systems in the U.S. and nearly 50% of all medical imaging. We are recognized as a promising healthcare AI company by CB Insights and AuntMinnie, and ranked by Deloitte as the 19th fastest-growing company in North America. We are building AI-powered solutions that make a real impact.

Requirements

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 Haves

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.

Responsibilities

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.