GPU Systems Infrastructure Engineer

Blue Signal Search; Fremont (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Blue Signal Search seeks a hands-on engineer to own the NVIDIA GPU compute environment lifecycle, from architecture through rollout and ongoing support.

You will build GPU-accelerated platforms for AI, ML, and high-performance workloads while integrating compute servers, storage, and networking into reliable end-to-end infrastructure.

Qualifications

• Experience designing and validating GPU-accelerated infrastructure.
• Proven ability to deploy and support GPU compute environments.
• Understanding of GPUs integration with servers, storage, and network infrastructure.

Responsibilities

• Own the technical lifecycle of NVIDIA GPU compute environments from architecting to rollout and support.
• Build and refine GPU-accelerated platforms for AI, ML and HPC workloads.
• Integrate compute servers, workstations, storage, and networking into end-to-end infrastructure.
• Validate configurations through benchmarking, stress testing, and troubleshooting.
• Collaborate with customer-facing and commercial teams to translate workload needs into solutions.
• Evaluate evolving GPU, server, storage, networking, and cluster tech for future platform decisions.
• Demonstrate initiative and accountability, working independently with strong communication.

Skills

GPU computing

HPC

System validation

Benchmarking

Performance tuning

Networking

Storage

Cluster compute

Problem solving

Ownership

Education

Bachelor’s degree in CS or related field

Tools

NVIDIA GPUs

Job description

Our client develops and supports sophisticated technology infrastructure built to handle complex, performance-intensive applications. They are seeking a hands-on engineer who can bring together NVIDIA GPU technology, compute platforms, storage, networking, and clustered infrastructure into high-performing solutions. This role offers the opportunity to work across the full systems lifecycle, solve challenging technical problems, and make a meaningful impact in an environment where initiative, technical curiosity, and personal ownership are highly valued.

This Role Offers

• Work with diverse technologies and solutions that span multiple areas of sophisticated technical infrastructure.
• Exposure to a diverse range of advanced systems, platforms, and supporting infrastructure technologies.
• A team-oriented culture that encourages proactive problem-solving, personal ownership, clear communication, and dependable execution.

Focus

• Own the technical lifecycle of NVIDIA GPU compute environments, from architecture and component selection through validation, rollout, and ongoing support.
• Build and refine GPU-accelerated platforms for demanding AI, machine learning, and high-performance computing workloads.
• Integrate compute servers, engineering workstations, storage, and network components into reliable end-to-end infrastructure.
• Validate new configurations through benchmarking, stress testing, performance analysis, and structured troubleshooting.
• Identify and resolve technical challenges across various system environments while ensuring issues are addressed through completion.
• Partner with customer-facing and commercial teams to translate workload needs into practical, supportable technical solutions.
• Evaluate evolving GPU, server, storage, networking, and cluster technologies to inform future platform decisions.
• Demonstrate initiative and accountability while working independently and maintaining strong communication and teamwork.

Skill Set

• Hands-on experience working with a range of modern systems and technical solutions.
• Demonstrated experience designing, validating, deploying, and supporting GPU-accelerated infrastructure.
• Strong server and compute systems foundation, including an understanding of how GPUs integrate with the surrounding platform and infrastructure.
• Experience working across technologies such as servers, technical workstations, storage platforms, networking, and multi-node compute environments.
• Proven ability to identify and resolve complex technical challenges across a variety of systems and environments.
• Exposure to benchmarking, performance tuning, system validation, or workload optimization in accelerated computing environments.
• Strong problem-solving skills with the ability to move from technical investigation to practical resolution.
• High level of commitment, ownership, self-direction, collaboration, and communication.
• Several years of relevant experience in GPU computing, HPC, AI platforms, or large-scale infrastructure is preferred, but demonstrated technical capability and depth of experience will carry greater weight than a specific tenure threshold.
• Bachelor’s degree in computer science, Engineering, or a related technical discipline is preferred, or equivalent relevant experience.