Staff ML Platform Engineer (AWS/Databricks)

Location: Remote - United States

Salary: $224,000–$280,000 USD per year

Company

hirify.global is a healthcare data collaboration platform that provides secure data solutions for providers, health plans, researchers, and life sciences organizations.

What you will do

• Set technical direction for ML training, serving, observability, GPU capacity, Spark tuning, and production incident resolution.
• Evolve the shared CI/CD spine, model-workflow scaffolding, and Databricks Asset Bundles into a self-service ML platform.
• Lead architecture for LLM endpoint serving across Databricks, AWS, Snowflake, and self-hosted deployments, including latency, cost, caching, evaluation, and PHI-safe routing.
• Define standards and tooling for MLflow, model registries, training image supply chains, and training and inference observability.
• Partner with Data Science, Application Development, Operations, vendors, and platform teams on architecture and technology selection.
• Mentor senior engineers while remaining hands-on with production code and Infrastructure-as-Code.

Requirements

• 10+ years of software engineering experience, including 3+ years designing and operating enterprise-scale ML platforms in production.
• Production experience with Databricks and/or Amazon SageMaker, MLflow or an equivalent system, and a core ML framework such as PyTorch or TensorFlow.
• Strong Java or JVM-equivalent and Python skills, with deep Apache Spark experience in large-scale distributed computing.
• Deep AWS experience across networking, IAM, GPU compute, storage, and messaging services.
• Fluency with Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads.
• Production LLM serving experience, including cost management, evaluation, and safe handling of sensitive prompts and outputs.

Nice to have

• Technical leadership on healthcare or regulated-industry ML platforms involving HIPAA, HITRUST, SOC 2, or equivalent standards.
• Experience with Databricks Asset Bundles, Unity Catalog, Iceberg, Delta, Kafka, or Kinesis.
• Experience with specialized inference pipelines, GPU capacity planning, real-time inference, safety-sensitive AI evaluation, or open-source ML infrastructure.

Culture & Benefits

• Collaborative, high-performance environment focused on transforming healthcare through data logistics products and services.
• Remote work in the United States.
• Full-time employment with total rewards compensation.
• Post-offer health screenings and vaccination requirements may apply depending on client and state requirements.
• Reasonable accommodations are available for candidates with disabilities.