ML Engineer: Healthcare Data Pipelines & Model Workflows

School of Medicine; Palo Alto (CA); Sur place; USD 123 000 - 145 000; Plein temps

Résumé du poste

Stanford University in Palo Alto seeks a Machine Learning Engineer to advance biomedical AI research under the ARPA-H/BDF grant. You will build end-to-end data pipelines and modular ML training/inference infrastructure for multiple downstream applications, guiding tool choices and platform design.

Reporting to the technical manager, you will collaborate with scientists and engineers to deploy robust systems and evaluate model performance, safety, and transparency in real-world contexts.

Qualifications

• Bachelor’s degree in engineering, science, or related field and 3+ years of relevant experience.
• Experience with ML platforms and healthcare data.
• Ability to supervise and train a diverse staff.

Responsabilités

• Build end-to-end data pipelines and infrastructure for ML models.
• Develop scalable training and inference infrastructure for various applications.
• Collaborate with scientists and engineers to oversee complex analyses.
• Design and implement ML model evaluation and deployment frameworks.
• Ensure health and safety policies and training guidelines are followed.
• Lead code reviews and oversee project objectives.

Connaissances

PyTorch

JAX

Hugging Face

Gradio

Streamlit

Python

BigQuery SQL

CI/CD

MLOps

Healthcare data

Cloud infrastructure

GitHub

Formation

Bachelor’s degree in engineering or science

Outils

Docker

GitHub

Description du poste

Stanford University in Palo Alto seeks a Machine Learning Engineer to advance biomedical AI research under the ARPA-H/BDF grant. You will build end-to-end data pipelines and modular ML training/inference infrastructure for multiple downstream applications, guiding tool choices and platform design.

Reporting to the technical manager, you will collaborate with scientists and engineers to deploy robust systems and evaluate model performance, safety, and transparency in real-world contexts.