Staff ML Platform Engineer

Title: Staff ML Platform Engineer Location: Remote - United States

What We’re Looking For

We are looking for a Staff ML Platform Engineer to join our Data & ML Platform organization on the ML Platform team. We own the paved road that takes a model from notebook to production without rebuilding it each time: training and inference across SageMaker and Databricks, self-service LLM-endpoint serving, and the pipelines behind Datavant’s clinical-AI products. Data Science owns the models and their quality; we own the pipelines, serving infrastructure, and operational guardrails that let those models run safely against regulated health data.

As a Staff ML Platform Engineer, you will be a technical leader across the ML Platform: setting direction for the paved road, owning the hardest architectural problems, and moving the team from bespoke plumbing toward a coherent platform that Data Science teams can self-serve. AI fluency is a baseline expectation. You should already be using Claude Code, Cursor, Copilot, or equivalent tools as a core part of your daily engineering workflow, have opinions about how they make a team faster, and know how to apply them responsibly when PHI and other sensitive data are in scope.

What You Will Do

• 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

What You Need to Succeed

• 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

What Helps You Stand Out

• Prior technical leadership on a healthcare or regulated-industry ML platform (HIPAA, HITRUST, SOC 2, or equivalent)
• Direct experience with Databricks Asset Bundles, Unity Catalog, and open table formats (Iceberg, Delta)
• Production experience with specialized inference pipelines beyond generic model serving (e.g., document understanding, computer vision, or streaming inference)
• GPU capacity planning at strategic, tactical, and operational horizons
• Experience with real-time and event-driven inference and streaming platforms (Kafka, Kinesis)
• Evaluation and red-teaming experience for clinical or safety-sensitive AI
• Background contributing to open-source ML infrastructure or publishing on production ML systems