This role integrates comprehensive experimentation and software engineering to explore how emerging platforms respond under demanding HPC and AI workloads. You will reveal performance restrictions, establish trustworthy measurements, and provide collaborating teams with the insights they need to improve new platforms.
Compensation
The expected base salary ranges for this position are: Poland: 292,500 PLN – 507,000 PLN for Level 4; and 375,000 PLN – 650,000 PLN for Level 5. France: 87,750 EUR – 152,100 EUR for Level 4; and 114,000 EUR – 197,600 EUR for Level 5. Germany: 112,200 EUR – 194,480 EUR for Level 4; and 141,900 EUR – 245,960 EUR for Level 5.
Responsibilities
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 their impact on GPU usage and application efficiency.
Examine distributed workload scaling, considering collective communication, synchronization delays, slow nodes, and the interplay between computation and communication.
Lead performance engineering for NVIDIA’s data center platforms, integrating experimentation and software to optimize HPC and AI workloads.
Compensation
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:
Poland: 292,500 PLN – 507,000 PLN for Level 4; and 375,000 PLN – 650,000 PLN for Level 5.
Responsibilities
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
Examine distributed workload scaling, considering collective communication, synchronization delays, slow nodes, and the interplay between computation and communication
Combine application profiles, system traces, hardware counters, and cluster telemetry to isolate root causes
Requirements
BS or MS in Computer Science, Engineering, Mathematics, Physics (or equivalent experience)
Lead end‑to‑end performance engineering for NVIDIA’s CSP Engagements, driving performance characterization, tooling, and optimization across hyperscale customers.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
Responsibilities
Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers
Gather and synthesize CSP performance feedback
Ensure key open‑source performance and stress tools are updated and validated
Work closely with CSPs to ensure their own performance and validation tooling reflects the latest GPU capabilities
Conduct cross‑CSP performance comparison and pattern analysis
Requirements
BS or MS in Computer Science, Computer Engineering, or related field (or equivalent experience)
Nice to have: profiling and optimizing distributed training at 1000+ GPU scale
Nice to have: ML infrastructure performance at a CSP/hyperscaler
Nice to have: familiarity with NVIDIA platforms (DGX, HGX, NVLink topology)
Nice to have: building automated performance regression detection systems
Lead end‑to‑end performance engineering for NVIDIA’s CSP engagements, driving performance characterization, tooling, and optimization across hyperscale customers.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
Responsibilities
Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers
Gather and synthesize CSP performance feedback
Ensure key open‑source performance and stress tools are updated and validated
Work closely with CSPs to ensure their own performance and validation tooling reflects the latest GPU capabilities
Conduct cross‑CSP performance comparison and pattern analysis
Requirements
BS or MS in Computer Science, Computer Engineering, or related field (or equivalent experience)
Nice to have: profiling and optimizing distributed training at 1000+ GPU scale
Nice to have: ML infrastructure performance at a CSP/hyperscaler
Nice to have: NVIDIA platforms (DGX, HGX, NVLink topology)
Nice to have: automated performance regression detection systems
Lead inference performance for Runware’s AI Factory, owning kernel development, benchmarking, and team leadership across GPU, HPC, and deep learning systems.
Responsibilities
Own inference performance end to end for the fleet
Set the model performance roadmap
Write and tune CUDA kernels, fuse attention and MLP paths
Build pipelines for new model deployment
Choose and shape the serving stack
Requirements
Nice to have: Diffusion and video generation performance
Define and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.
Benchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.
Analyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.
Lead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
Evaluate, benchmark, profile, and optimize AI inference workloads across modern CPU, GPU, APU, and heterogeneous computing platforms.
Responsibilities
Benchmark and characterize AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related technologies.
Analyze latency, throughput, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, operating systems, memory subsystems, and hardware.
Optimize workload performance through quantization, graph optimization, kernel tuning, memory optimization, and heterogeneous compute utilization.
Conduct competitive benchmarking across AMD and competing CPU, GPU, APU, and AI accelerator platforms.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related field
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