Senior ML Platform Engineer

Datavant; Austin (TX); 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, owning the paved road from training to production. You will drive architecture for training, serving, and observability, partnering with Data Science, App Dev, and Operations.

This role requires deep AWS, Spark, and ML tooling experience, plus AI-native workflows and async communication. You will mentor engineers, stay hands-on with IaC, and shape vendor selections while enabling self-serve production workflows for

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

Java

Python

Spark

Databricks

SageMaker

MLflow

PyTorch

TensorFlow

Terraform

Kubernetes

CI/CD for ML

LLM tooling

Tools

Databricks Asset Bundles

Unity Catalog

Iceberg

Delta

Job description

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team, owning the paved road from training to production. You will drive architecture for training, serving, and observability, partnering with Data Science, App Dev, and Operations.

This role requires deep AWS, Spark, and ML tooling experience, plus AI-native workflows and async communication. You will mentor engineers, stay hands-on with IaC, and shape vendor selections while enabling self-serve production workflows for