Pronto Staff Engineer at Pronto focuses on building and scaling HPC and AI compute infrastructure for autonomous systems. The role involves hands-on work with hardware, software, networking, and storage from design through production deployment.
Responsibilities
As a Staff Engineer focused on HPC & AI Compute Infrastructure, you’ll help build the physical computing infrastructure powering Atoms’ AI, robotics, and autonomous systems. You'll work closely with our Infrastructure, AI, and engineering teams to bring immediate GPU capacity online while architecting the foundation for thousands of GPUs, large-scale CPU compute, and Exabyte-scale storage. This is a hands-on role spanning hardware, software, networking, storage, and data-center infrastructure, where you'll own complex technical challenges from initial requirements through deployment and production.
Build AI compute infrastructure from the ground up. Lead the design, deployment, and commissioning of physical GPU and HPC infrastructure, including an intermediate data-center environment in San Francisco, to support autonomous systems, AI/ML workloads, and large-scale simulation.
Architect for scale. Define the technical foundation to grow from initial deployments to thousands of GPUs, large-scale CPU compute, and Exabyte-scale storage, balancing performance, reliability, scalability, and cost.
Own compute and hardware architecture. 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. Work across cluster interconnects, high-bandwidth networking, distributed storage, and data movement to support compute-intensive workloads.
Partner on physical data-center delivery. Collaborate with facilities, real estate, and infrastructure teams to translate compute requirements into power, cooling, rack density, space, and connectivity specifications.
Drive infrastructure deployment end to end. Work with hardware manufacturers, vendors, and internal teams on technical evaluations, bills of materials, procurement planning, installation, integration, validation, and production readiness.
Automate infrastructure operations. Develop tooling and processes for bare-metal provisioning, configuration management, monitoring, hardware validation, incident response, and failure recovery to make deployments repeatable and reliable.
Own capacity planning and operational reliability. Anticipate infrastructure bottlenecks, troubleshoot complex hardware and software issues, and continuously improve performance, utilization, and availability as compute demand grows.
Qualification
Bachelor's degree in Computer ScienceDemonstrated experience designingFamiliarity with NVIDIA DGX/HGX systemsExposure to modular data centers
Required
We're looking for a hands-on infrastructure engineer who has experience building and scaling physical compute environments, from initial system design through deployment and production. You should be comfortable working across hardware, software, networking, storage, and data-center infrastructure, bringing deep technical expertise and the ability to lead complex engineering initiatives. We're looking for someone who can move quickly to deliver immediate compute capacity while building the technical foundation for long-term scale.
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
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 working with high-performance or large-scale computing environments, including GPU/CPU clusters, distributed computing, AI/ML infrastructure, simulation, or comparable workloads.
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 infrastructure requirements, including power, cooling, rack density, cabling, and network connectivity.
Strong troubleshooting skills and experience diagnosing complex production issues across hardware, operating systems, networking, and storage.
Ability to operate independently in fast-moving environments, navigate ambiguity, collaborate across engineering disciplines, and drive technical decisions without relying on established processes.
Preferred
Experience building or operating large-scale AI/ML training clusters, GPU supercomputers, or high-density HPC environments.
Experience in industries such as 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 directly with hardware OEMs, infrastructure vendors, procurement teams, and facilities partners on capacity planning, infrastructure deployments, and capital expenditure decisions.
