Systems Performance Engineer, AI Solutions Lab

About the Role

The AI Solutions Lab within Marvell's Platform organization is responsible for the validation layer that proves Marvell's AI and cloud infrastructure platforms perform as promised across compute, networking, and memory fabrics. This team runs AI benchmarking experiments, operates GPU/HPC lab clusters, validates Marvell's Teralynx switching and AI fabrics, and builds live demos. Engineers work across Marvell's AI platform portfolio, including Teralynx network fabrics, integrated optical platforms like CPO and NPO, and emerging memory fabric products such as CXL and photonic fabric memory appliances.

Key Responsibilities

• Drive end-to-end performance optimization across AI serving and training stacks using leading models and customer-relevant configurations.
• Identify bottlenecks across the full data path, from application request flow through compute, memory, communication layers, GPU kernels, network transport, and data/storage pipelines.
• Develop and execute benchmark methodologies that are reproducible, technically credible, and relevant to customer use cases.
• Profile serving-stack internals — quantization, scheduling, KV-cache management, and disaggregated prefill/decode — to identify concrete optimization targets.
• Analyze results and develop performance narratives that guide kernel, runtime, communication, and system-level optimizations.
• Work closely with NRE / SRE and network or fabric architects to close competitive gaps through targeted tuning.
• Create performance summaries, visuals, and benchmark artifacts for customer presentations, executive reviews, and trade-show demonstrations.

Requirements & Tech Stack

• Experience with AI serving frameworks such as vLLM, SGLang, TensorRT-LLM, or similar platforms.
• Strong background in RCCL/NCCL, RDMA, communication-compute overlap, and performance profiling.
• Hands-on experience with any GPU profiling tools (e.g., Nsight Systems/Compute, PyTorch Profiler, or ROCm Compute Profiler) to localize bottlenecks at the kernel level.
• Demonstrated ability to diagnose end-to-end bottlenecks across application, framework, runtime, and infrastructure layers.
• Working knowledge of at least one distributed training framework (PyTorch DDP/FSDP, DeepSpeed, or Megatron-LM).
• Strong Python and Linux skills; Go experience is a plus.
• Comfortable working in containerized environments (Docker; Kubernetes a plus) to ensure benchmark reproducibility.
• Ability to translate deep technical analysis into clear recommendations and concise performance narratives.

Preferred Qualifications

• Experience benchmarking distributed large-model inference and training systems.
• Familiarity with GPU kernel analysis, system tracing, and workload-specific optimization.
• Familiarity with serving-stack internals such as continuous batching, paged KV-cache attention, and quantization techniques (e.g., GPTQ, SmoothQuant).
• Exposure to customer proofs of concept, competitive benchmarking, or externally consumed performance reporting.

Education

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field; a PhD is a plus.