ML Engineer: Healthcare Foundation Models

Stanford University School of Medicine; Palo Alto (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Stanford University School of Medicine's Weill Cancer Hub seeks a Machine Learning Engineer to develop AI/ML methods for biomedical applications, focusing on rigorous evaluation in real-world settings and practical deployment.

You will build end-to-end data pipelines and software infrastructure for ML research, design modular training, evaluation, and inference systems, and guide engineering decisions across data and software projects.

Qualifications

• Advanced ML knowledge and hands-on experience in biomedical data.
• Experience turning research into production ML pipelines.
• Strong software engineering practices and code quality.

Responsibilities

• Develop end-to-end ML pipelines from data ingestion to deployment.
• Collaborate with scientists and engineers to translate research into scalable systems.
• Design and implement training, evaluation, and inference workflows.
• Guide tool choices and engineering best practices across data and software projects.

Skills

Machine Learning

Python

Data Pipelines

Healthcare

Collaboration

Education

Bachelor's or equivalent in CS/Engineering

Tools

TensorFlow

PyTorch

SQL

Job description

Stanford University School of Medicine's Weill Cancer Hub seeks a Machine Learning Engineer to develop AI/ML methods for biomedical applications, focusing on rigorous evaluation in real-world settings and practical deployment.

You will build end-to-end data pipelines and software infrastructure for ML research, design modular training, evaluation, and inference systems, and guide engineering decisions across data and software projects.