Staff ML Platform Engineer: Scale Production ML

Datavant; Olympia (WA); On-site; USD 224,000 - 280,000; Full time

Job summary

Datavant is seeking a Staff ML Platform Engineer to lead our ML Platform team, owning the paved road for training, serving, and observability. You will drive architectural decisions, enable self-serve pipelines, and address challenging infrastructure problems across Databricks, AWS, and Snowflake.

You’ll mentor engineers, contribute code and IaC, and shape an AI-native workflow with tools like Claude Code, Cursor, and Copilot while ensuring PHI-safe handling in production.

Qualifications

• 10+ years of software engineering with 3+ years in enterprise-scale ML platforms.
• Experience with Databricks and/or SageMaker, MLflow, and a core ML framework (PyTorch or TensorFlow).
• Fluency in Java and Python with strong Spark experience.
• AWS expertise including networking, IAM, GPU compute, and storage/messaging services.

Responsibilities

• Set technical direction across ML training, serving, and observability, acting as escalation point for infrastructure issues.
• Own and evolve the paved-road framework for Data Science to production workflows.
• Lead architecture for LLM-endpoint serving across providers and self-hosted deployments.
• Own standards for MLflow, model registry, training image supply chain, and observability.
• Partner with Data & ML Platform teammates to present a cohesive platform to teams.
• Contribute to vendor/platform decisions on model providers and tooling.
• Mentor senior engineers and contribute hands-on IaC and code.

Skills

ML platform experience

Databricks/SageMaker

Python

Java

Apache Spark

Terraform

Kubernetes

AWS expertise

LLM tooling

Tools

Databricks

MLflow

Unity Catalog

Iceberg/Delta

GitHub Actions

Job description

Datavant is seeking a Staff ML Platform Engineer to lead our ML Platform team, owning the paved road for training, serving, and observability. You will drive architectural decisions, enable self-serve pipelines, and address challenging infrastructure problems across Databricks, AWS, and Snowflake.

You’ll mentor engineers, contribute code and IaC, and shape an AI-native workflow with tools like Claude Code, Cursor, and Copilot while ensuring PHI-safe handling in production.