1 day ago
Base Salary
$184k - $357k/yr
Responsibilities
- Build shared self-service and validation mechanisms for supported infrastructure workflows.
- Provide diagnostic views across application, submission, remote-execution, host, and target layers.
- Set performance and reliability benchmarks and use profiling, metrics, traces, and controlled experiments to evaluate changes.
- Improve compute and GPU selection, scheduling, placement, concurrency, worker and runner configuration, resource sharing, caching, and input/output paths.
- Analyze retry causes, recovery outcomes, wasted execution capacity, and recurring failures.
- Implement and validate infrastructure changes with application, build and CI, remote-execution, and infrastructure teams.
- Measure improvements in turnaround time, efficiency, stability, usable capacity, and cost.
- Turn successful investigations into supported workflows, diagnostics, documentation, and customer handoffs.
Requirements
- More than 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 and experience delivering tested changes into production infrastructure.
- In-depth Linux experience and 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 collaborate across teams, develop clear technical plans from ambiguous symptoms, and explain results to engineering customers.
- A degree in computer science, computer engineering, or a related field, or equivalent experience.
- Preferred experience includes GPU sharing and isolation, CUDA execution behavior, MIG, MPS, GPU driver diagnostics, high-performance computing, workload placement, CPU and GPU concurrency, NUMA, cloud-instance benchmarking, Bazel, remote execution, simulation or replay workloads, hardware-in-the-loop validation, high-throughput data paths, customer self-service, observability, and incident or retry analysis.
Benefits
- Competitive salary, equity, and comprehensive benefits.
- Hybrid work arrangement, as indicated by the posting's #LI-Hybrid designation.
- Applications are accepted at least until October 23, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.