AI Infra Engineer

HealthLeap•San Francisco, CA

2d•$200,000 - $275,000•Hybrid

About The Position

HealthLeap builds AI that helps clinicians prioritize patients, surfaces the right data, and gets patients the care they need earlier, so they can leave the hospital sooner. We integrate with hospital electronic health record systems, screen 100% of patients daily, and risk-rank them in real time. Clinicians at Cedars-Sinai and Penn Medicine start every morning with HealthLeap — with Houston Methodist, Emory, and Intermountain Health deploying now. Real results: 39% more diagnoses. 4 days earlier detection. $11M/year ROI for our first site at Cedars Sinai. 7× revenue growth in 7 months. We started with malnutrition. We're expanding to every major condition to ensure no patient falls through the cracks. Sequoia and First Round are backing us to build the platform that screens every patient for everything and drives tangible outcomes. We're ~15 people. >$7M raised. SF-based, hybrid-friendly. Early enough to shape the product. Late enough to know it works. Results that are changing lives. Build the infrastructure that screens every hospital patient, every day. HealthLeap processes billions of data points from hospital EHRs. Data flows through secure connections, transforms through data pipelines, and powers ML models that clinicians rely on every morning. You'll work alongside our data scientists and ML engineers to build and operate the infrastructure that makes this possible. Why you You've built AI infrastructure at a startup where you owned everything. The person who set up the AWS accounts, wrote the Terraform, deployed the ML models, and debugged the VPN at 2am. You've taken models from Jupyter notebooks to production. You're the person who figures things out when the docs don't help.

Requirements

5+ years engineering experience, with significant infrastructure work
Deep AWS experience: networking, IAM, ECS/EKS, S3, and the rest
Solid with Terraform or similar infrastructure-as-code
Experience with data pipelines and orchestration (Dagster, Airflow, or similar)
You've operated production systems, not just built them
You figure things out, even when documentation doesn't exist

Nice To Haves

MLOps experience: deploying and monitoring ML models in production
Healthcare data standards (FHIR, HL7v2)
Experience with Kubernetes, service mesh, or complex networking
Background at an early-stage startup where you owned infra end-to-end

Responsibilities

Deploy new hospitals: site-to-site VPNs, networking, container services, data pipelines
Build reliable data infrastructure that ingests, transforms, and serves data at scale (S3, Iceberg, Spark, Dagster, Airflow)
Partner with data scientists and ML engineers to productionize models and get them running reliably at scale
Own AWS infrastructure: multi-account orgs, ECS/EKS, Terraform, networking
Set up observability: monitoring, alerting, logging that actually helps debug issues
Make hospital deployments faster and more reliable