Staff Engineer, Infrastructure – HPC & AI Compute

ATOMS Careers page•San Francisco, CA

1d•$224,000 - $284,000•Onsite

About The Position

Pronto AI is hiring a Staff Engineer focused on HPC and AI compute infrastructure to build the physical computing infrastructure that powers AI, robotics, and autonomous systems. The role will work closely with Infrastructure, AI, and engineering teams to bring GPU capacity online and establish a foundation for scaling to thousands of GPUs, large-scale CPU compute, and Exabyte-scale storage. This hands-on role spans hardware, software, networking, storage, and data-center infrastructure. The engineer will own complex technical challenges from requirements through deployment and production, including designing and commissioning physical GPU and HPC infrastructure and an intermediate data-center environment in San Francisco. The position is based in the San Francisco office and is onsite five days a week. The base salary range is $224,000–$284,000 per year. Actual compensation may vary based on experience, skills, and qualifications. The total rewards package may also include equity awards. Benefits listed for USA full-time exempt employees include medical, dental, vision, disability, and life insurance; flexible spending and health savings account options; a 401(k); equity; sick time, unlimited flexible time off, and paid holidays; paid parental leave; a pre-tax commuter benefit plan; and team lunch in the SoMa office every Tuesday and Thursday. Benefits may change at the company’s discretion.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
At least 8 years of experience in infrastructure engineering, systems engineering, high-performance computing, data-center engineering, or related technical disciplines.
Demonstrated experience designing, deploying, commissioning, or significantly expanding physical compute infrastructure, including HPC clusters, GPU systems, or large-scale data-center environments.
Proven ability to lead complex infrastructure initiatives from technical requirements and architecture through implementation, production readiness, and ongoing operations.
Strong systems engineering foundation across Linux, server hardware, networking, and storage, with deep technical expertise in multiple areas.
Experience with high-performance or large-scale computing environments, such as GPU/CPU clusters, distributed computing, AI/ML infrastructure, or simulation.
Hands-on experience developing infrastructure automation or tooling using Python, Go, Bash, C++, Ansible, or similar technologies.
Experience evaluating hardware configurations, networking and storage technologies, vendor solutions, and performance, reliability, and cost tradeoffs.
Working knowledge of data-center requirements, including power, cooling, rack density, cabling, and network connectivity.
Strong troubleshooting skills and experience diagnosing production issues across hardware, operating systems, networking, and storage.
Ability to work independently in fast-moving environments, navigate ambiguity, collaborate across engineering disciplines, and drive technical decisions without relying on established processes.

Nice To Haves

Experience building or operating large-scale AI/ML training clusters, GPU supercomputers, or high-density HPC environments.
Experience in AI infrastructure, autonomous vehicles, robotics, scientific computing, semiconductor engineering, hyperscale data centers, or GPU cloud infrastructure.
Familiarity with NVIDIA DGX/HGX systems, InfiniBand, RDMA/RoCE, Slurm, Kubernetes, or other HPC and distributed computing technologies.
Experience with distributed storage technologies such as Ceph, Lustre, GPFS/Spectrum Scale, BeeGFS, or similar systems.
Experience with bare-metal provisioning and cluster automation using PXE, xCAT, Warewulf, NVIDIA Base Command Manager, or comparable tools.
Exposure to modular data centers, liquid cooling, high-density rack deployments, and large-scale power and cooling infrastructure.
Experience working with hardware OEMs, infrastructure vendors, procurement teams, and facilities partners on capacity planning, infrastructure deployments, and capital expenditure decisions.

Responsibilities

Lead the design, deployment, and commissioning of physical GPU and HPC infrastructure, including an intermediate data-center environment in San Francisco, for autonomous systems, AI/ML workloads, and large-scale simulation.
Define the technical foundation for scaling to thousands of GPUs, large-scale CPU compute, and Exabyte-scale storage while balancing performance, reliability, scalability, and cost.
Evaluate and specify GPU servers, CPUs, memory, networking components, storage systems, and rack configurations based on workload requirements and infrastructure constraints.
Design high-performance networking and storage, including cluster interconnects, high-bandwidth networking, distributed storage, and data movement.
Work with facilities, real estate, and infrastructure teams to translate compute requirements into power, cooling, rack density, space, and connectivity specifications.
Coordinate technical evaluations, bills of materials, procurement planning, installation, integration, validation, and production readiness with hardware manufacturers, vendors, and internal teams.
Develop tooling and processes for bare-metal provisioning, configuration management, monitoring, hardware validation, incident response, and failure recovery.
Plan capacity, anticipate infrastructure bottlenecks, troubleshoot hardware and software issues, and improve performance, utilization, and availability.
Lead infrastructure initiatives from requirements and architecture through implementation, production readiness, and ongoing operations.