Senior Systems Software Engineer, Workload Systems Engineering

Reposted 7 Days Ago
Be an Early Applicant
Santa Clara, CA, USA
Hybrid
184K-357K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develops self-service infrastructure, diagnostics, and validation mechanisms for build, simulation, and testing workloads. Investigates performance and reliability across applications, remote execution, compute resources, GPUs, storage, and host systems. Uses profiling, metrics, traces, benchmarks, and controlled experiments to improve scheduling, resource utilization, caching, data paths, recovery, efficiency, stability, and cost. Collaborates across engineering teams to implement, validate, document, and operationalize infrastructure improvements.
Summary Generated by Built In

NVIDIA is seeking a senior systems software engineer to help engineering teams run demanding build, simulation, and validation workloads efficiently and reliably. Our Workload Systems Engineering team connects customer workflows with the infrastructure that performs them. We develop self-service mechanisms and clear diagnostics, investigate performance and reliability across system boundaries, and deliver measured infrastructure improvements with the teams that own the underlying components.


Your responsibilities include enabling application configuration, remote execution, workers, hosts, GPU and CPU resources, storage, data paths, and on-target systems. The position requires direct system evaluation: diagnosing causes of slow or failing workloads, trying alternatives in representative scenarios, applying improvements with others, and verifying customer advantages.


What you'll be doing:

  • Build shared self-service and validation mechanisms that let engineering teams configure and use supported infrastructure workflows with less manual assistance.
  • Give customers and on-call teams useful diagnostic views across application, submission, remote-execution, and host or target layers so they can identify where failures and slowdowns originate.
  • Set performance and reliability benchmarks for representative workloads. Apply profiling, metrics, traces, and controlled experiments to pinpoint the changes that make a difference.
  • Evaluate and improve compute and GPU selection, scheduling and placement, concurrency, worker and runner configuration, resource sharing, caching, and input/output paths.
  • Make retry causes, recovery outcomes, and wasted execution capacity visible. Work with component owners to eliminate recurring failures and unnecessary re-execution.
  • Implement and validate infrastructure-level changes with application, build and CI, remote-execution, and infrastructure teams. Measure the effect on turnaround, successful efficiency, stability, usable capacity, and cost.
  • Turn successful investigations into supported workflows, diagnostics, documentation, and clear customer handoffs.

What we need to see:

  • Over 8 years of solid software engineering experience working with production compute infrastructure, distributed systems, large-scale build and test systems, or comparable systems software.
  • Hands-on experience debugging complex performance or reliability issues across software and hardware boundaries using logs, metrics, traces, profiling, and experiments.
  • Proficiency in a systems programming or automation language and experience delivering tested changes into production infrastructure.
  • In-depth experience with Linux and proficiency in one or more of these fields: GPU systems and drivers, scheduling and resource management, remote execution or CI infrastructure, storage and data paths, or host and container infrastructure.
  • Ability to work across teams with different component ownership, turn ambiguous symptoms into a clear technical plan, and explain results to engineering customers.
  • A degree in computer science, computer engineering, or a related field, or equivalent experience.

Ways to stand out from the crowd:

  • Experience with GPU sharing and isolation, CUDA execution behavior, MIG or MPS or equivalent experience, GPU driver diagnostics, or high-performance computing workloads.
  • Experience with workload placement, CPU and GPU concurrency, NUMA, cloud-instance benchmarking, or cost and performance tradeoffs.
  • Familiarity with Bazel, remote execution, build front ends, simulation or replay workloads, and hardware-in-the-loop validation.
  • Experience improving file-system, caching, networking, or high-throughput data paths under concurrent workloads.
  • A record of building customer self-service or observability tools and using incident and retry data to drive verified reliability improvements.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/


#LI-Hybrid

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Over 8 years of software engineering experience with production compute infrastructure, distributed systems, large-scale build and test systems, or comparable systems software
  • Hands-on experience debugging complex performance or reliability issues across software and hardware boundaries using logs, metrics, traces, profiling, and experiments
  • Proficiency in a systems programming or automation language
  • Experience delivering tested changes into production infrastructure
  • In-depth experience with Linux
  • Proficiency in GPU systems and drivers, scheduling and resource management, remote execution or CI infrastructure, storage and data paths, or host and container infrastructure
  • Ability to work across teams, create technical plans from ambiguous symptoms, and explain results to engineering customers
  • Degree in computer science, computer engineering, or a related field, or equivalent experience
  • Experience with GPU sharing and isolation, CUDA execution behavior, MIG, MPS, GPU driver diagnostics, or high-performance computing workloads
  • Experience with workload placement, CPU and GPU concurrency, NUMA, cloud-instance benchmarking, or cost and performance tradeoffs
  • Familiarity with Bazel, remote execution, build front ends, simulation or replay workloads, and hardware-in-the-loop validation
  • Experience improving file systems, caching, networking, or high-throughput data paths under concurrent workloads
  • Record of building customer self-service or observability tools and using incident and retry data to drive verified reliability improvements

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
  • Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
  • Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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