Senior ML Platform Engineer - Scale Production AI

Datavant; Richmond (VA); On-site; USD 224,000 - 280,000; Full time

Job summary

Datavant in the United States is seeking a Staff ML Platform Engineer to lead the paved-road for ML infrastructure, spanning training, serving, and observability across SageMaker, Databricks, and open source tools. You will set technical direction and mentor engineers while delivering self-serve platforms for Data Science teams.

This role demands 10+ years in software, deep AWS experience, and hands-on work with MLflow, Spark, and containerized CI/CD.

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, influencing peers, and raising the bar across teams.
• Hands-on production experience with Databricks and/or Amazon SageMaker, MLflow (or an equivalent tracking + registry system), and at least one core ML framework (PyTorch, TensorFlow, or similar).
• Fluency in Java (or a JVM equivalent) and Python, with real depth in Apache Spark for large-scale data and distributed compute.
• Real depth in AWS: networking, IAM, GPU compute, and the storage and messaging services this role touches, with the judgment to know what to reach for and when
• Fluency with Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads
• Direct experience serving LLMs in production, including cost management, evaluation harnesses, and safe handling of sensitive prompts and outputs
• AI-native working style: daily use of Claude Code, Cursor, Copilot, or equivalent, with views on how they make a team faster
• Clear written and verbal communication, especially in async, remote settings

Responsibilities

• Set technical direction across ML training, serving, and observability, and be the final escalation point for the most elusive infrastructure problems (GPU capacity, Spark tuning, production incidents)
• Own and evolve our paved-road framework (the shared CI/CD spine, model-workflow scaffolding, and Databricks Asset Bundles) so Data Science teams can go from a config file to a production workflow without bespoke plumbing
• Lead architecture for LLM-endpoint serving across managed providers (Databricks, AWS, Snowflake) and self-hosted deployments, covering latency, cost, caching, evaluation, and PHI-safe routing
• Own the standards and tooling for MLflow, model registry, training image supply chain, and observability across training and inference
• Partner closely with your Data & ML Platform teammates to present a cohesive ML platform to the Data Science, App Dev, and Operations teams at Datavant
• Serve as a key technical input to vendor and platform selection decisions across model providers, ML tooling, and observability
• Mentor senior engineers on the team, provide technical guidance to platform consumers, and stay hands-on writing high-leverage code and Infrastructure-as-Code alongside your teammates

Skills

Software engineering

ML platform design

Technical judgment

Communication (written/verbal)

LLM production experience

Tools

Databricks

Amazon SageMaker

MLflow

PyTorch

TensorFlow

Apache Spark

Terraform

Kubernetes

GitHub CI/CD

Claude Code / Cursor / Copilot

Job description

Datavant in the United States is seeking a Staff ML Platform Engineer to lead the paved-road for ML infrastructure, spanning training, serving, and observability across SageMaker, Databricks, and open source tools. You will set technical direction and mentor engineers while delivering self-serve platforms for Data Science teams.

This role demands 10+ years in software, deep AWS experience, and hands-on work with MLflow, Spark, and containerized CI/CD.