HPC Engineer - AI Infrastructure

Hamilton Barnes Associates Limited; San Francisco (CA); On-site; USD 235,000 - 315,000; Full time

Benefits offered by this job

Founding engineer equity

Full benefits package

Job summary

Hamilton Barnes Associates Limited is building a founding engineering team for a VC-backed GPU cloud platform in San Francisco. This hands-on role owns core infrastructure spanning Kubernetes, Slurm, GPU orchestration, and high-speed storage, collaborating with founders to shape architecture and scale production AI systems.

You will influence the platform's design, reliability, and performance while helping define engineering culture and hiring as an early team member.

Qualifications

• 3 to 6 years of hands-on experience operating large-scale AI or HPC infrastructure in production.
• Proven experience operating large-scale Kubernetes and Slurm clusters.
• Experience building and managing GPU orchestration layers on top of core schedulers.
• Strong understanding of distributed object storage, NVMe storage clusters, and high-bandwidth networking.
• Deep knowledge of storage architectures for large-scale AI infrastructure.
• Comfort operating with founding-level ownership across the full infrastructure stack.
• Based in or willing to relocate to San Francisco.

Responsibilities

• Design, build, and operate large-scale Kubernetes and Slurm based GPU clusters in production
• Build and manage GPU orchestration layers on top of core scheduling infrastructure
• Architect and operate distributed object storage, NVMe storage clusters, and storage systems for large-scale AI workloads
• Design and manage high-bandwidth networking supporting distributed training and inference at scale
• Design, deploy, and optimise a scalable inference stack for serving large AI models with low-latency, high-throughput GPU acceleration
• Build telemetry, observability, and automated remediation across the GPU fleet
• Implement power-aware infrastructure mechanisms including GPU power capping, power-aware scheduling, and telemetry loops
• Own operational health, reliability, and performance of the platform end to end
• Work directly with founders on architecture, roadmap, and technical strategy
• Help define engineering culture, standards, and hiring as one of the first technical team members

Skills

Kubernetes

Slurm

GPU orchestration

Distributed storage

NVMe storage

High-bandwidth networking

Telemetry/observability

Founding-level ownership

Willing to relocate to SF

Tools

Kubernetes

Slurm

NVMe storage

Distributed storage systems

Telemetry tooling

Networking knowledge

Job description

Ready to take the next step in your career?

Join a VC-backed GPU cloud company at the founding stage, building the next generation of managed GPU compute and inference infrastructure. Backed by leading venture investors, the organization delivers fully managed AI infrastructure and offers the opportunity to help shape the technical foundation of a rapidly growing GPU cloud platform.

This company is seeking a Founding Engineer to take ownership of core GPU cloud infrastructure across Kubernetes, Slurm, GPU orchestration, distributed storage, high-bandwidth networking, and inference platforms. This hands-on role offers founding-level ownership, significant influence over platform architecture, and the opportunity to work closely with the founders to build and scale production AI infrastructure from the ground up.

Responsibilities:

• Design, build, and operate large-scale Kubernetes and Slurm based GPU clusters in production
• Build and manage GPU orchestration layers on top of core scheduling infrastructure
• Architect and operate distributed object storage, NVMe storage clusters, and storage systems for large-scale AI workloads
• Design and manage high-bandwidth networking supporting distributed training and inference at scale
• Design, deploy, and optimise a scalable inference stack for serving large AI models with low-latency, high-throughput GPU acceleration
• Build telemetry, observability, and automated remediation across the GPU fleet
• Implement power-aware infrastructure mechanisms including GPU power capping, power-aware scheduling, and telemetry loops
• Own operational health, reliability, and performance of the platform end to end
• Work directly with founders on architecture, roadmap, and technical strategy
• Help define engineering culture, standards, and hiring as one of the first technical team members

Skills/Must Have:

• 3 to 6 years of hands-on experience operating large-scale AI or HPC infrastructure in production
• Proven experience operating large-scale Kubernetes and Slurm clusters
• Experience building and managing GPU orchestration layers on top of core schedulers
• Strong understanding of distributed object storage, NVMe storage clusters, and high-bandwidth networking
• Deep knowledge of storage architectures for large-scale AI infrastructure
• Comfort operating with founding-level ownership across the full infrastructure stack
• Based in or willing to relocate to San Francisco

Benefits:

• Founding engineer equity
• Full benefits package

Salary:

• $275,000 Base