Senior ML Platform Engineer - Production-Ready Pipelines

Datavant2; United States; Remote; USD 224,000 - 280,000; Full time

Job summary

Datavant is seeking a Staff ML Platform Engineer to lead the Data & ML Platform's paved road, including training, serving, and observability. You will drive architectural direction, own the framework, and guide Data Science teams toward self-serve production workflows with Databricks, AWS, and self-hosted deployments.

Responsibilities include setting standards for MLflow, registries, training image supply chains, and observability; partnering with teams to present a cohesive platform; mentoring

Qualifications

• 10+ years of software engineering experience.
• 3+ years designing, evolving, and operating enterprise-scale ML platforms in production.
• Hands-on production experience with Databricks and/or Amazon SageMaker, MLflow (or an equivalent tracking + registry system).
• Fluency in Java 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.
• 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.

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

Databricks

SageMaker

MLflow

Python

Java

Spark

Terraform

Kubernetes

CI/CD

LLM serving

Tools

Databricks

SageMaker

MLflow

Terraform

Kubernetes

Spark

Job description

Datavant is seeking a Staff ML Platform Engineer to lead the Data & ML Platform's paved road, including training, serving, and observability. You will drive architectural direction, own the framework, and guide Data Science teams toward self-serve production workflows with Databricks, AWS, and self-hosted deployments.

Responsibilities include setting standards for MLflow, registries, training image supply chains, and observability; partnering with teams to present a cohesive platform; mentoring