Staff Site Reliability Engineer
Confirmed on the employer's own hiring board on Oct 21, 2026. First seen by Alion on Sep 28, 2026. Hippocratic AI scores B on the Alion truth index.
Join our team as a Staff Site Reliability Engineer, where you'll take ownership of managing a large fleet of GPU-backed models. You'll design and build our GPU management and scheduling platform, implement admission control, and develop cloud orchestration systems. This is a senior role for someone with over 10 years of experience in site reliability and software engineering.
Missions
- Design and build the GPU management and scheduling platform, including the metrics pipeline and admission control logic.
- Implement autoscaling that adjusts the number of model replicas in response to real-time demand and utilization.
- Architect and operate scalable, fault-tolerant, secure production systems on AWS, GCP, or Azure, and develop infrastructure automation and deployment pipelines.
Profil recherché
- Strong software engineering fundamentals - you build orchestration and scheduling systems in Python and/or Go, not just configure off-the-shelf tools- 10+ years of professional experience across site reliability / DevOps engineering and software engineering
- Strong knowledge of containerization and orchestration (Docker, Kubernetes)
- Experience with monitoring and logging stacks (ELK, Grafana, Datadog, or similar)
- Excellent problem-solving skills and the ability to work both independently and collaboratively
- Computer Science Degree Required from a top CS program
- 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
- Hands-on production experience with at least one major cloud platform (AWS, GCP, or Azure)
- Deep experience with infrastructure automation and CI/CD (Terraform, GitLab CI/CD, or similar)
- Familiarity with secrets management and security tooling (HashiCorp Vault, AWS KMS, Azure Key Vault)
- Strong communication and interpersonal skills
- 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 implementing HIPAA and SOC 2 compliance
- Experience with Kubernetes autoscaling internals (HPA/VPA, custom metrics, custom controllers)
- Experience operating in an HPC environment
- Bachelor's or Master's in Computer Science, Computer Engineering, or a related field