Senior Site Reliability Engineer (SRE) - AI Inftastructure

Hamilton Barnes Associates Limited; San Francisco (CA); On-site; USD 270,000 - 330,000; Full time

Benefits offered by this job

Equity

Job summary

A stealth-mode startup in San Francisco seeks a Platform Engineer/Senior Site Reliability Engineer to manage their AI and cloud platform. You will design and maintain large-scale GPU clusters, create automation pipelines, and enhance system reliability. Ideal candidates have over 7 years of experience in SRE or DevOps, strong skills in Kubernetes, Linux systems, and automation using Python or Go. This role offers a gross salary of $300,000 annually along with equity options.

Qualifications

• 7+ years of experience in SRE, DevOps, or Infrastructure Engineering roles.
• Deep knowledge of Linux systems, networking, and GPU infrastructure.
• Strong hands-on experience with Kubernetes and Slurm.

Responsibilities

• Design, deploy, and maintain large-scale GPU clusters.
• Build automation pipelines for provisioning, scaling, and monitoring resources.
• Develop observability and auto-healing systems for high-availability workloads.

Skills

SRE experience

Kubernetes

Slurm

Linux systems

Python

Go

Bash

Prometheus

Grafana

Loki

Job description

Join a stealth-mode startup building out their AI and cloud platform, powered by thousands of H100s, H200s, and B200s, ready for experimentation, full-scale model training, or inference. As a Platform Engineer/Senior Site Reliability Engineer, you’ll own the reliability, performance, and automation of this GPU-powered infrastructure, ensuring seamless orchestration across environments managed by Slurm, Kubernetes, or direct SSH access. As well as supporting their extremely exciting new products coming to the market!

This is a rare opportunity to work at the intersection of AI infrastructure and AI, shaping the operational backbone of one of the largest GPU clusters in private deployment.

If you want to build and operate infrastructure for frontier AI workloads, automate systems at petascale, and be part of a founding engineering team, this is the place to do it. Get in touch and apply today!

Responsibilities:

• Design, deploy, and maintain large-scale GPU clusters (H100/H200/B200) for training and inference workloads.
• Build automation pipelines for provisioning, scaling, and monitoring compute resources across Slurm and Kubernetes environments.
• Develop observability, alerting, and auto-healing systems for high-availability GPU workloads.
• Collaborate with ML, networking, and platform teams to optimise resource scheduling, GPU utilisation, and data flow.
• Implement infrastructure-as-code, CI/CD pipelines, and reliability standards across thousands of nodes.
• Diagnose performance bottlenecks and drive continuous improvements in reliability, latency, and throughput.

Skills / Must Have:

• 7+ years of experience in SRE, DevOps, or Infrastructure Engineering roles supporting large-scale compute environments.
• Strong hands-on experience with Kubernetes and Slurm for cluster orchestration and workload management.
• Deep knowledge of Linux systems, networking, and GPU infrastructure (NVIDIA H100/H200/B200 preferred).
• Proficiency in Python, Go, or Bash for automation, tooling, and performance tuning.
• Experience with observability stacks (Prometheus, Grafana, Loki) and incident response frameworks.
• Familiarity with high-performance computing (HPC) or AI/ML training infrastructure at scale.
• Background in reliability engineering, distributed systems, or hardware acceleration environments is a strong plus.

Salary & Benefits:

• $300,000 gross per year
• Equity