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Rad AI

Generative AI radiology workflow automation platform

Machine Learning Research Manager

AI ResearchConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A Master of Science or PhD in Computer Science, Machine Learning, Computational Linguistics, Biomedical Informatics, or a related quantitative field, or equivalent practical experience.
  • At least 6 years of applied machine learning research experience, with a track record of taking work from idea to production impact.
  • Prior people management experience, or clear evidence of operating as a de facto team lead for multi-person research efforts, with coaching and prioritization skills.
  • A strong background in natural language processing and modern deep learning, especially fine-tuning transformer-based systems and large language models.
  • Experience applying machine learning to hard real-world problems where ambiguity, data quality, and operational constraints matter.
  • Hands-on experience with modern machine learning tooling such as PyTorch and common model training and evaluation workflows.
  • Ability to collaborate closely with domain experts and cross-functional stakeholders, especially in environments where trust, iteration speed, and communication quality matter.
  • Written and verbal communication skills sufficient to guide senior researchers and align non-technical partners around research direction and tradeoffs.

Responsibilities

  • Directly manage and mentor a small team of research scientists, helping them grow in scope, judgment, execution, and communication.
  • Drive research productivity by clarifying priorities, unblocking work, and creating a high-trust, high-accountability team environment.
  • Guide problem framing, experimental design, training and evaluation strategy, and tradeoff decisions across multiple research efforts while staying close to the technical work.
  • Partner with radiologists, clinical experts, engineers, and product leaders to identify high-leverage research opportunities and translate them into production-scale systems.
  • Help the team train and evaluate advanced natural language processing and reasoning systems that work with clinical text, diagnostic criteria, reporting workflows, and other healthcare data.
  • Create cross-functional working rhythms with engineering, product, and clinical partners so research outputs are practical, trustworthy, and deployable.
  • Raise research quality, reproducibility, and communication across the team.
  • Stay current on relevant machine learning advances and help the team integrate new methods when they materially improve customer and clinical outcomes.

Desired Qualifications

  • Experience working in healthcare, clinical artificial intelligence, biomedical machine learning, or other regulated, privacy-sensitive environments.
  • Experience with retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), group relative policy optimization (GRPO), and other modern model training techniques.
  • Experience with radiology, clinical documentation, medical terminology, or clinician-facing workflows.
  • Experience deploying large language model or natural language processing systems in production settings.
  • Experience contributing to research culture through mentorship, technical standards, or organizational leadership.
  • Familiarity with cloud-based machine learning workflows and modern research infrastructure.

About the Company

Rad AI

Generative AI radiology workflow automation platform

Rad AI builds AI-powered tools for radiology to streamline clinical workflows. Its enterprise SaaS platform integrates with existing radiology software to automate reporting and follow-up processes. Core products include Rad AI Reporting (generates radiology reports tailored to a radiologist’s style), Rad AI Impressions (summaries of dictated findings), and Rad AI Continuity (automated follow-up management for incidental findings). The company targets large health systems and leading radiology practices, aiming to reduce physician burnout and improve patient care by speeding up documentation and standardizing follow-up.

Company size
201-500
Company stage
Series C
Total funding
$147M
Headquarters
San Francisco, CA
Founded
2018

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