Senior ML Platform Engineer - Production-Grade Pipelines

Datavant; Honolulu (HI); Remote; USD 224,000 - 280,000; Full time

Job summary

Datavant is seeking a Staff ML Platform Engineer to lead our ML Platform team, guiding the paved road from training to production. You will shape architecture for end-to-end ML workflows, including LLM-endpoint serving across Databricks, AWS, and self-hosted deployments, while mentoring engineers and contributing hands-on IaC.

The role requires deep experience with Databricks, SageMaker, MLflow, and a strong background in Spark, Python, and Java.

Qualifications

• 10+ years of software engineering experience
• 3+ years designing, evolving, and operating enterprise-scale ML platforms in production
• Hands-on with Databricks and/or SageMaker, MLflow, and at least one core ML framework (PyTorch or TensorFlow)
• Fluency in Java and Python, with depth in Spark for large-scale data
• AWS experience including networking, IAM, GPU compute, and related services
• Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads
• Experience serving LLMs in production with cost management and safe handling of prompts
• AI-native working style with daily use of Claude Code, Cursor, Copilot
• Strong written and verbal communication in async, remote settings

Responsibilities

• Set technical direction across ML training, serving, and observability
• Own and evolve paved-road framework for Data Science to production workflows
• Lead architecture for LLM-endpoint serving across managed providers and self-hosted deployments
• Own standards and tooling for MLflow, model registry, training image supply chain
• Collaborate with Data & ML Platform teams to present a cohesive platform
• Influence vendor/platform selection for ML tooling and observability
• Mentor engineers and contribute hands-on to code and IaC

Skills

Java

Python

Spark

MLOps

AI Platform

Tools

Databricks

SageMaker

MLflow

Spark

Terraform

Kubernetes

GitHub CI/CD

Job description

Datavant is seeking a Staff ML Platform Engineer to lead our ML Platform team, guiding the paved road from training to production. You will shape architecture for end-to-end ML workflows, including LLM-endpoint serving across Databricks, AWS, and self-hosted deployments, while mentoring engineers and contributing hands-on IaC.

The role requires deep experience with Databricks, SageMaker, MLflow, and a strong background in Spark, Python, and Java.