Staff ML Platform Engineer: Production AI Infra Leader

Datavant; Salem (OR); On-site; USD 224,000 - 280,000; Full time

Job summary

Datavant is hiring a Staff ML Platform Engineer to lead the ML platform effort, setting direction for training, serving, and observability while owning the paved-road framework and best practices. You will mentor engineers and contribute hands-on to scalable, compliant pipelines for regulated health data.

You will work across Databricks, SageMaker, MLflow, Spark, Terraform, and Kubernetes to enable self-serve ML workloads, including LLM endpoints, with a focus on safety, cost, and performance.

Qualifications

• 10+ years of software engineering experience with 3+ years designing, evolving, and operating enterprise-scale ML platforms.

Responsibilities

• Set technical direction across ML training, serving, and observability, and be the final escalation point for infrastructure problems.
• Own and evolve our paved-road framework (CI/CD spine, model-workflow scaffolding, Databricks Asset Bundles).
• Lead architecture for LLM-endpoint serving across providers and self-hosted deployments, covering latency, cost, and PHI-safe routing.
• Own standards for MLflow, model registry, training image supply chain, and observability.
• Partner with Data & ML Platform teammates to present a cohesive ML platform to Data Science, App Dev, and Operations teams at Datavant.
• Serve as a technical input to vendor/platform decisions across model providers, ML tooling, and observability.
• Mentor senior engineers and stay hands-on with Infrastructure-as-Code.

Skills

Software engineering

ML platform design

Cloud (AWS)

Spark

Python

Java

Terraform

Kubernetes

MLflow

LLMs

Tools

Databricks

SageMaker

MLflow

Spark

Terraform

Kubernetes

GitHub CI/CD

Job description

Datavant is hiring a Staff ML Platform Engineer to lead the ML platform effort, setting direction for training, serving, and observability while owning the paved-road framework and best practices. You will mentor engineers and contribute hands-on to scalable, compliant pipelines for regulated health data.

You will work across Databricks, SageMaker, MLflow, Spark, Terraform, and Kubernetes to enable self-serve ML workloads, including LLM endpoints, with a focus on safety, cost, and performance.