Senior Systems Software Engineer, Workload Systems Engineering

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-s