ML Engineer: Healthcare Data Pipelines & MLOps

Abundance Network; United States; On-site; USD 123,000 - 145,000; Full time

Job summary

Stanford University seeks a Machine Learning Engineer to perform advanced technical research for the ARPA-H/BDF grant. You will build end-to-end data pipelines and ML model infrastructure, enabling training and inference for biomedical applications.

You will collaborate with scientists and senior engineers, design software and data infrastructure, and develop robust, modular systems while ensuring safety and reliability in models for real-world deployment.

Qualifications

• Bachelor’s degree in engineering, science, or related field and three years of relevant experience; or a combination of education and relevant experience.
• Knowledge and skills of advanced scientific or engineering principles.
• Experience applying complex scientific and engineering principles and performing technical services.

Responsibilities

• Support complex scientific and research programs; analyze data and design prototypes or systems.
• Collaborate with scientists and engineers to oversee analyses and optimize solutions.
• Independently troubleshoot routine problems for researchers and engineers.
• Collaborate to design and develop special purpose equipment and systems.
• Plan, design, and implement scientific or engineering initiatives and drive project objectives.
• Oversee a laboratory space and supervise technicians and staff.

Skills

PyTorch

JAX

CI/CD

MLOps

Python

BigQuery SQL

Healthcare data

GitHub

HIPAA

Publications

Education

Bachelor’s degree in engineering, science, or related field

Tools

Docker

Job description

Stanford University seeks a Machine Learning Engineer to perform advanced technical research for the ARPA-H/BDF grant. You will build end-to-end data pipelines and ML model infrastructure, enabling training and inference for biomedical applications.

You will collaborate with scientists and senior engineers, design software and data infrastructure, and develop robust, modular systems while ensuring safety and reliability in models for real-world deployment.