ML Engineer — Biomedical Data Pipelines & Model Ops

Stanford University School of Medicine; Palo Alto (CA); On-site; USD 123,000 - 145,000; Full time

Job summary

Stanford University is seeking a Machine Learning Engineer to perform advanced technical research for the ARPA-H/BDF grant. The role involves building end-to-end data pipelines and modular ML infrastructures for training and inference across biomedical applications with a focus on safety, reliability, and transparency.

You will work with clinicians and researchers to implement ML models, evaluate performance, and guide tool/language choices for scalable data projects in a HIPAA-regulated

Qualifications

• Bachelor’s degree in engineering, science, or related field; 3 years of relevant experience.
• Experience with ML frameworks PyTorch or JAX.
• Experience with cloud infrastructure and CI/CD.
• Experience with healthcare data and HIPAA.
• Ability to write Python and BigQuery SQL.
• Publications in AI for medical applications is a plus.

Responsibilities

• Support complex scientific and research programs; analyze data and develop prototypes.
• Collaborate with scientists and engineers to oversee analyses and design systems.
• Troubleshoot problems and independently resolve routine issues.
• Design and develop software and data infrastructure for analytics.
• Oversee lab space and supervise technicians.
• Develop training manuals and safety guidelines.
• Review research proposals and health and safety policies.

Skills

Python

Machine learning

Data pipelines

HIPAA experience

Team collaboration

BigQuery SQL

Education

Bachelor’s degree in engineering or science

Tools

PyTorch

JAX

Hugging Face

gradio

streamlit

Docker

GitHub

Job description

Stanford University is seeking a Machine Learning Engineer to perform advanced technical research for the ARPA-H/BDF grant. The role involves building end-to-end data pipelines and modular ML infrastructures for training and inference across biomedical applications with a focus on safety, reliability, and transparency.

You will work with clinicians and researchers to implement ML models, evaluate performance, and guide tool/language choices for scalable data projects in a HIPAA-regulated