Staff Site Reliability Engineer

Hippocratic AI•Menlo Park, CA

About The Position

We're looking for a Senior Site Reliability Engineer who is equally at home writing production software and running the infrastructure it lives on — and who wants to take ownership of one of the hardest, highest-leverage problems on our platform: intelligently managing a large fleet of GPU-backed models. We run nearly 30 models across heterogeneous hardware, and keeping that fleet fast, reliable, and cost-effective is a serious engineering challenge. You'll build the GPU management and scheduling platform that sits at the center of it — collecting utilization and load metrics, interpreting what they actually mean, and using them to make real-time decisions about admission control and scaling. The goal: route and schedule inference calls so we use our capacity efficiently without exceeding it, and scale model replicas up and down automatically as demand shifts. This is a senior role for someone with a decade in the field who can move fluidly between systems engineering and software development, and who is excited to own a complex, evolving system end to end.

Requirements

10+ years of professional experience across site reliability / DevOps engineering and software engineering
Computer Science Degree Required from a top CS program.
Strong software engineering fundamentals — you build orchestration and scheduling systems in Python and/or Go, not just configure off-the-shelf tools
Experience designing systems that make decisions from operational metrics — collecting signals, interpreting them, and driving control loops such as autoscaling, load shedding, or admission control
Deep experience with infrastructure automation and CI/CD (Terraform, GitLab CI/CD, or similar)
Hands-on production experience with at least one major cloud platform (AWS, GCP, or Azure)
Strong knowledge of containerization and orchestration (Docker, Kubernetes)
Experience with monitoring and logging stacks (ELK, Grafana, Datadog, or similar)
Familiarity with secrets management and security tooling (HashiCorp Vault, AWS KMS, Azure Key Vault)
Excellent problem-solving skills and the ability to work both independently and collaboratively
Strong communication and interpersonal skills

Nice To Haves

Experience managing GPU fleets or scheduling workloads across heterogeneous accelerators
Familiarity with ML inference serving and model deployment (e.g. Triton, KServe, Ray Serve, or similar)
Experience with Kubernetes autoscaling internals (HPA/VPA, custom metrics, custom controllers)
Experience implementing HIPAA and SOC 2 compliance
Experience operating in an HPC environment
Bachelor's or Master's in Computer Science, Computer Engineering, or a related field

Responsibilities

Design and build our GPU management and scheduling platform — the system that decides when, where, and how inference calls run across a fleet of ~30 models on heterogeneous hardware
Build the metrics pipeline that collects GPU load and utilization data, and the logic that turns those signals into decisions
Implement admission control to protect capacity — deciding when to accept, queue, or shed inference requests so we operate within fleet limits
Build autoscaling that adjusts the number of model replicas in response to real-time demand and utilization
Develop cloud orchestration systems and operators in Python and Go to manage the model fleet
Architect and operate scalable, fault-tolerant, secure production systems on AWS, GCP, or Azure
Design and build infrastructure automation and deployment pipelines (Terraform, CI/CD) as first-class software
Stand up and maintain monitoring, logging, and alerting that keep the platform reliable and performant
Develop and enforce security and compliance policies appropriate to a healthcare AI platform
Partner with engineers and research scientists to diagnose and resolve complex infrastructure, deployment, and operational issues
Mentor engineers and raise the technical bar across the team