Healthcare ML Engineer: End-to-End Data Pipelines & Models

Stanfordlivetickets; Palo Alto (CA); Hybrid; USD 123,000 - 145,000; Full time

Benefits offered by this job

Health benefits

Tuition assistance

Flexible work options

403(b) plan

Job summary

Stanford University is seeking a Machine Learning Engineer to build end-to-end data pipelines and modular software for training and inference of ML models used in healthcare research.

The role reports to the technical manager of the ARPA-H/BDF grant and involves collaborating with scientists and engineers to design scalable pipelines, evaluate models, and ensure safety, reliability, and transparency for real-world deployment.

Qualifications

• Bachelor’s degree in engineering, science, or related field with relevant experience.
• Demonstrated knowledge and skills of advanced scientific or engineering principles and practices.
• Experience applying complex scientific and engineering principles and performing technical services.

Responsibilities

• Build end-to-end data pipelines and software infrastructures for training and inference of ML models.
• Collaborate with scientists and engineers to oversee complex analyses and design solutions.
• Develop training manuals, safety guidelines, and train staff in instrumentation use.

Skills

Python

ML Ops

Healthcare data

GitHub collaboration

Education

Bachelor's degree in engineering/science

Tools

Docker

PyTorch

JAX

Hugging Face

Streamlit

Gradio

BigQuery

Job description

Stanford University is seeking a Machine Learning Engineer to build end-to-end data pipelines and modular software for training and inference of ML models used in healthcare research.

The role reports to the technical manager of the ARPA-H/BDF grant and involves collaborating with scientists and engineers to design scalable pipelines, evaluate models, and ensure safety, reliability, and transparency for real-world deployment.