What youll be doing:
Profile, benchmark, and analyze AI and HPC workloads on GPU and CPU clusters.
Explore performance characteristics of high-performance networking and collective communications (e.g., NCCL, RDMA, MPI, RoCE).
Identify performance bottlenecks across networking, compute, memory, and system architecture.
Develop and enhance performance analysis, benchmarking, and diagnostic tools.
Define performance test plans and establish expectations for new technologies and platforms.
Collaborate across hardware, firmware, networking, systems, and software teams to provide actionable performance insights.
Support telemetry collection and data refinement efforts to enable accurate performance analysis.
Maintain high standards for data quality, reproducibility, and traceability of performance results.
What we need to see:
B.Sc. or M.Sc. in Computer Science, Computer Engineering, Software Engineering, or equivalent experience.
5+ years of experience in performance analysis, systems engineering, or HPC/AI infrastructure.
Demonstrated expertise in performance analysis skills and methodologies.
Hands-on experience with high-performance networking (RDMA, MPI, NCCL, congestion control).
Strong understanding of system performance metrics (latency, throughput, resource utilization).
Exposure to hardware, firmware, or embedded telemetry environments.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively in cross-functional, fast-paced R&D teams.
Ways to stand out from the crowd:
Knowledge of CUDA, NCCL internals, and congestion control algorithms.
Deep system-level understanding of CPU architectures, GPUs, HCAs, memory, and PCIe.
Experience with NVIDIA GPUs, CUDA, and deep learning frameworks such as PyTorch or TensorFlow.
Experience with cloud platforms.
Proficiency in Python; experience with Bash and C/C++ is a plus as well as a strong experience working in Linux environments.
