Staff ML Research Scientist, Applied Healthcare AI

Rad AI; San Francisco (CA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Medical, dental, vision & life

HSA with employer match

401(k)

Paid holidays

Flexible PTO

Company offsite

Equipment stipend

Job summary

Rad AI is seeking a Staff Machine Learning Research Scientist to define and drive applied research in NLP and clinical AI. You will work across LLMs, retrieval, representation learning, speech and multimodal modeling, ensuring reliable, production-scale systems with strong evaluation.

You will collaborate with clinicians, engineers, and product leaders to translate research into impact, shaping model quality, safety, and observability while contributing to strategic initiatives in computer

Qualifications

• MS or PhD (or equivalent) in CS/EE/CL/BI or related field.
• 7+ years of applied ML research experience (Staff level impact).
• Expertise in LLMs, NLP, CV, speech, or multimodal modeling.
• Strong hypothesis framing, offline-online linkage, and ablations.

Responsibilities

• Own end-to-end applied research from framing to production.
• Set technical direction across LLMs, retrieval and multimodal work.
• Build meaningful evaluation linking offline metrics to online outcomes.
• Collaborate with engineers, product, and clinicians to align goals.
• Mentor peers and codify reliability, safety, and observability standards.
• Improve platform for data, training, serving and reproducibility.
• Explore new directions including vision-language initiatives.

Skills

Staff-level ML research

Applied ML

Production-ready models

Experimentation rigor

PyTorch

ML tooling (MLflow/Databricks/Ray/Trit

Education

MS or PhD in Computer Science, Electrical Engineering, Computational Linguistics, Biomedical Informatics, or related field

Tools

PyTorch

MLflow

Databricks

Ray

Triton

Job description

Rad AI is seeking a Staff Machine Learning Research Scientist to define and drive applied research in NLP and clinical AI. You will work across LLMs, retrieval, representation learning, speech and multimodal modeling, ensuring reliable, production-scale systems with strong evaluation.

You will collaborate with clinicians, engineers, and product leaders to translate research into impact, shaping model quality, safety, and observability while contributing to strategic initiatives in computer