Senior AI/ML Engineer — Healthcare Data at Scale

Metriport; San Francisco (CA); Hybrid; USD 170,000 - 260,000; Full time

Benefits offered by this job

Equity and competitive compensation

Full family Platinum health insurance,

401(k) with matching

Flexible work from home or in-office

Healthy lunches when in-office

Quarterly off-sites

MacBook provided

Unlimited PTO

Job summary

Metriport in the San Francisco Bay Area is seeking an experienced ML Engineer to own end-to-end machine learning on rich clinical datasets. You will frame problems with customers, train models, and deploy them in production while keeping them honest and measurable.

Expect to work on scalable ML pipelines, feature engineering, and real-world data challenges, collaborating closely with founders and engineers in a fast-moving, autonomous team.

Qualifications

• 7+ years building ML systems that run on real data at scale.
• Strong grounding across regression/tree methods, feature engineering and deep learning.
• Track record of ML delivering measurable results in production.
• Solid software engineering: cloud backends, training pipelines, serving and monitoring.
• Strong data skills: SQL and large datasets, building data pipelines.
• Based in or willing to relocate to San Francisco Bay Area.

Responsibilities

• Build models and the ML platform to turn raw clinical data into actionable intelligence.
• Own the full ML lifecycle: framing problems, training, evaluation, deployment, monitoring.
• Turn unstructured clinical data into structured records; parse PDFs and notes.
• Stand up ML infrastructure: pipelines, experiment tracking, model versioning, evaluations.
• Collaborate with founders, engineers, and customers to identify high-leverage ML problems.

Skills

ML fundamentals

Production ML

Python

AWS

SQL

Software eng

Tools

TypeScript

CDK

IaC

SQL

AWS

Job description

Metriport in the San Francisco Bay Area is seeking an experienced ML Engineer to own end-to-end machine learning on rich clinical datasets. You will frame problems with customers, train models, and deploy them in production while keeping them honest and measurable.

Expect to work on scalable ML pipelines, feature engineering, and real-world data challenges, collaborating closely with founders and engineers in a fast-moving, autonomous team.