Staff ML Platform Engineer: Scale Production ML Pipelines

Datavant; Salt Lake City (UT); On-site; USD 224,000 - 280,000; Full time

Job summary

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team within Data & ML Platform. You will set technical direction for training, serving, and observability, owning the paved road so Data Science teams can self-serve from config to production workflows.

Ideal candidates have 10+ years in software engineering and 3+ years building enterprise-grade ML platforms, with hands-on Databricks/SageMaker, MLflow, and AWS expertise.

Qualifications

• 10+ years software engineering experience with ML platforms in production.
• Strong judgment and ability to set standards across teams.
• Hands-on with Databricks/SageMaker and MLflow.
• Fluency in Java and Python; deep Spark experience.
• Extensive AWS knowledge including networking and GPU compute.
• Terraform, Kubernetes and GitHub CI/CD familiarity.
• Experience serving LLMs in production with cost management and safety controls.

Responsibilities

• Set direction across ML training, serving, and observability; resolve complex infra issues.
• Evolve paved-road framework for production workflows across DS teams.
• Lead architecture for LLM-endpoint serving across providers and self-hosted deployments.
• Own standards and tooling for MLflow, model registry, and observability.
• Collaborate with Data & ML Platform teammates to present a cohesive platform.
• Influence vendor and platform decisions for ML tooling and monitoring.
• Mentor senior engineers and contribute hands-on code and IaC.

Skills

10+ years software engineering

3+ years ML platform experience

Databricks / SageMaker

Java / Python

Spark / Apache Spark

Terraform / Kubernetes / CI/CD

LLM endpoints production

AI tooling (Claude Code / Cursor / Cop

Async comms in remote settings

Tools

MLflow

Databricks Asset Bundles

Unity Catalog

Job description

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team within Data & ML Platform. You will set technical direction for training, serving, and observability, owning the paved road so Data Science teams can self-serve from config to production workflows.

Ideal candidates have 10+ years in software engineering and 3+ years building enterprise-grade ML platforms, with hands-on Databricks/SageMaker, MLflow, and AWS expertise.