Staff Site Reliability Engineer, Hippocratic AI

• 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

Desirable

• We're looking for a Staff 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
• 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