Staff Software Engineer, Backend

Function Health is the AI operating system for health, designed to empower people to live 100 healthy years. They are seeking a Staff Software Engineer, Backend to build platform engineering solutions that enable subject matter experts to make changes independently and efficiently, ensuring data integrity and stability.

Responsibilities:

• Tracking infrastructure. The event pipeline behind product analytics, experimentation, and feature gates. Schemas that are enforced at the source, so a bad event never becomes a bad metric
• Data processing infrastructure. The Bronze → Silver → Gold layer in Databricks. Automated schema evolution, contract tests, backfills that aren't scary, freshness and volume monitors generated from the contract rather than bolted on after
• ML infrastructure. Feature computation and serving, training and eval pipelines, and the plumbing that gets model output back into the product — with the same testing and observability bar as everything else
• Cutting across all three. The self-service story. Templates, local dev and preview environments, policy-as-code for PHI, ownership routing for alerts, and progressive gates so an exploratory model ships freely while a member-facing one earns more scrutiny

Requirements:

• Built internal platform or infrastructure that other engineers actually adopted. You've felt the difference between shipping a tool and getting it used
• Operated production data or ML systems, on call for them, and fixed them under pressure
• Strong Python and SQL. Comfortable in a lakehouse – we use Databricks; Snowflake or BigQuery translates fine
• Designed interfaces and schemas that other teams depend on, then evolved them without breaking those teams
• Thought hard about testing and CI for data or ML, where correctness is statistical and the failure is often silent
• At the senior level, roughly 6+ years of engineering experience gets you here, but we care about what you've built, not the number
• Declarative pipeline frameworks (dbt, DLT, Dagster, Airflow)
• Streaming (Kafka, Spark Structured Streaming)
• Data contracts, data diffing, or lineage tooling
• Terraform
• Feature stores
• MLOps and eval tooling
• Agentic coding workflows
• Healthcare
• PHI, or HIPAA experience