Senior Systems Software Engineer - Data Center Performance

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SeniorZdalnie·WarszawaFull-time

#476990·Dodano 5 dni temu·23

Źródło: NVIDIA

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Tech Stack / Keywords

Linux Python C++CUDAHPCNsight SystemsNsight ComputeRDMAMPINCCL

Podsumowanie oferty

Join NVIDIA’s Applied Systems Engineering Performance team to investigate how emerging platforms perform under demanding HPC and AI workloads. Characterize and validate performance at datacenter scale using microbenchmarks, representative applications, and Python tools.

Wymagania

Extensive practical experience in Linux performance analysis, including processes and threads, CPU and memory architecture, NUMA, I/O, and networking.
Experience investigating performance in multi-node HPC or GPU-accelerated systems, distinguishing local resource bottlenecks from communication overhead, load imbalance, and shared-resource contention.
Deep expertise in at least one relevant area: GPU application performance, Linux systems performance, high-speed networking and collective communication, parallel storage, or HPC performance tooling.
Proficiency in Python to build maintainable automation and analysis tools. Experience developing and debugging performance-sensitive code in C, C++, and CUDA.
Ability to build controlled experiments, interpret profiling and counter data, and demonstrate reproducible improvements in application performance.
Ability to independently drive technical efforts across hardware and software boundaries, communicate findings clearly, and work with component owners to resolve issues.
BS or MS in Computer Science, Engineering, Mathematics, or Physics, or equivalent experience, with 8+ years of applicable experience.

Nice to have:

Experience with profiling and tracing tools such as Nsight Systems, Nsight Compute, Linux perf, or eBPF-based tools.
Practical experience with RDMA and MPI or NCCL, and analyzing performance across PCIe, NVLink, InfiniBand, or Ethernet interconnects.
Experience with Slurm, container runtimes, and cgroups, including how workload placement, resource isolation, and CPU/GPU/NIC affinity affect performance.
Experience improving distributed training, inference, or scientific applications through strong- and weak-scaling studies, or building automated performance regression frameworks using tools such as ReFrame.

Obowiązki

Performance characterization:

Characterize node, rack, and cluster performance using microbenchmarks and representative applications; measure throughput, latency, and scaling efficiency to identify compute, memory, communication, and storage bottlenecks.
Examine distributed workload scaling, including collective communication, synchronization delays, slow nodes, and the interplay between computation and communication. Assess how GPU and NIC positioning, interconnect structure, and network traffic affect performance.

Systems analysis and resolution:

Identify Linux performance problems related to CPU scheduling, NUMA locality, CPU and memory affinity, interrupt handling, and resource contention; assess their impact on GPU usage and application efficiency.
Combine application profiles, system traces, hardware counters, and cluster telemetry to isolate root causes. Develop focused reproductions and collaborate with hardware, firmware, driver, library, and application teams to implement and verify fixes.

Automation and validation:

Develop and maintain Python tools for automated benchmark execution, data collection, and performance analysis. Set up repeatable baselines, assess run-to-run variability, and identify regressions across hardware configurations and software releases.
Define and complete performance validation for new platforms and cluster deployments. Identify underperforming nodes, links, or storage paths, and verify that corrective actions restore workload performance.

Inne informacje

Remote work locations are Poland, Germany, and France.

NVIDIA

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