ML Engineer: Healthcare Data Pipelines & Model Workflows

SLAC

Palo Alto (CA)

Hybrid

USD 123,000 - 145,000

Full time

8 days ago

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Benefits offered by this job

Health benefits

PTO 18+ days/year

Flexible work options

Tuition & training support

403(b) plan

Mental health programs

Commuter benefits

Job summary

Stanford University seeks a Machine Learning Engineer to perform advanced research for ARPA-H/BDF, building data pipelines and modular ML software for training and inference on healthcare data. You will collaborate with scientists and engineers to evaluate model safety, reliability, and transparency for real-world deployment.

Reporting to grant management with dotted line to senior faculty, you will design tools, languages and platforms for scalable analytics while ensuring HIPAA compliance and

Qualifications

Ability to install, configure, and implement ML algorithms in training platforms (PyTorch, JAX) and inference platforms (Hugging Face, gradio, streamlit).
Experience with cloud infrastructure and CI/CD.
Experience with MLOps in healthcare data.
Proficiency writing Python and BigQuery SQL for large datasets.

Responsibilities

Build end-to-end data pipelines and infrastructure for ML models.
Develop modular software for training and inference of ML models.
Collaborate with scientists and engineers to design and implement initiatives.

Skills

ML algorithms

PyTorch

JAX

Cloud infrastructure

CI/CD

MLOps

Python

BigQuery SQL

Education

Bachelor’s degree in engineering/science or related field

Tools

Docker

GitHub

Job description

Reporting to grant management with dotted line to senior faculty, you will design tools, languages and platforms for scalable analytics while ensuring HIPAA compliance and

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