Senior ML Platform Engineer — Paved-Road AI Infra

Datavant; Hagåtña (GU); 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, driving the paved road from model development to production. You will own the architecture for training, serving, and observability, and work to make Data Science teams self-serve with a coherent platform across Databricks, AWS, and self-hosted deployments.

You will mentor engineers, shape standards for MLflow and registries, and collaborate with cross-functional teams to deliver secure, scalable ML infrastructure in

Qualifications

• 10+ years of software engineering experience, with 3+ years designing, evolving, and operating enterprise-scale ML platforms in production.
• Strong technical judgment under ambiguity and a track record of setting standards across teams.
• Hands-on production experience with Databricks and/or Amazon SageMaker, MLflow, and at least one core ML framework (PyTorch, TensorFlow, or similar).

Responsibilities

• Set technical direction across ML training, serving, and observability; escalate complex infra problems.
• Own and evolve paved-road framework for end-to-end production workflows.
• Lead architecture for LLM-endpoint serving across managed providers and self-hosted deployments.
• Own standards and tooling for MLflow, model registry, and training image supply chain.
• Partner with Data & ML Platform teammates to present cohesive platform to Data Science, App Dev, and Operations teams.
• Input to vendor and platform selection decisions for model providers and tooling.
• Mentor senior engineers and contribute hands-on code and IaC.

Skills

Software engineering

ML platform design

Databricks

SageMaker

Java

Python

Spark

AWS

Terraform

Kubernetes

LLM serving

CI/CD for ML

Communication

Tools

MLflow

Databricks Asset Bundles

Unity Catalog

Iceberg/Delta

Terraform

Job description

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team, driving the paved road from model development to production. You will own the architecture for training, serving, and observability, and work to make Data Science teams self-serve with a coherent platform across Databricks, AWS, and self-hosted deployments.

You will mentor engineers, shape standards for MLflow and registries, and collaborate with cross-functional teams to deliver secure, scalable ML infrastructure in