Senior Systems Software Engineer – Data Center Performance

NVIDIA

. Characterize node, rack, and cluster performance using microbenchmarks and representative applications .

Posted 11/9/2026full-timeRemote • Poland, Germany, FranceSenior💰 PLN 292,500 - PLN 650,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in Linux performance analysis, including CPU scheduling, NUMA locality, and I/O, while proficiently utilizing Python for automated benchmarking and performance tooling. Capable of driving technical efforts across hardware and software domains to enhance GPU application performance and optimize high-speed networking.

Highest-signal resume keywords

Linux Performance AnalysisPython ProficiencyGPU Application PerformanceHigh-Speed NetworkingPerformance Regression Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Linux Performance AnalysisPython ProgrammingC ProgrammingC++ ProgrammingCUDA ProgrammingPerformance ProfilingHPC Performance ToolingAutomated BenchmarkingPerformance ValidationData Analysis

Soft Skills

Technical CommunicationIndependent Problem SolvingCollaboration

Tools & Technologies

Nsight SystemsNsight ComputeLinux PerfEBPFRDMAMPINCCLSlurmContainer RuntimesCgroups

Industry Keywords

HPCGPU-Accelerated SystemsDistributed TrainingScientific ApplicationsPerformance Bottlenecks

Tech Stack

Tools & technologies

LinuxNode.jsPythonC++

About the role

Key responsibilities & impact

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
Identify Linux performance problems related to CPU scheduling, NUMA locality, CPU and memory affinity, interrupt handling, and resource contention
Assess the impact of Linux performance issues on GPU usage and application efficiency
Examine distributed workload scaling, collective communication, synchronization delays, slow nodes, and computation/communication interplay
Assess how GPU and NIC positioning, interconnect structure, and network traffic impact performance
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
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 corrective actions restore workload performance

Requirements

What you’ll need

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
Deep expertise in at least one of: GPU application performance, Linux systems performance, high-speed networking and collective communication, parallel storage, or HPC performance tooling
Proficiency in Python
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
Ability to independently drive technical efforts across hardware and software boundaries and communicate findings clearly
BS or MS in Computer Science, Engineering, Mathematics, or Physics, or equivalent experience
8+ years of applicable experience
Experience with Nsight Systems, Nsight Compute, Linux perf, or eBPF-based tools
Experience with RDMA and MPI or NCCL
Experience analyzing PCIe, NVLink, InfiniBand, or Ethernet interconnect performance
Experience with Slurm, container runtimes, and cgroups
Experience improving distributed training, inference, or scientific applications through strong- and weak-scaling studies
Experience building automated performance regression frameworks using tools such as ReFrame