Systems Performance Engineer, AI Solutions Lab

Marvell Technology • Santa Clara, CA

22d • $186,900 - $275,000

About The Position

The Systems Performance Engineer will work within Marvell’s AI Solutions Lab, part of the Platform organization. The team validates AI and cloud infrastructure across compute, networking, and memory fabrics, demonstrating performance in rack-scale, production-like conditions. Its work includes AI benchmarking, operating GPU/HPC lab clusters, validating Teralynx switching and AI fabrics, and building live demonstrations. The role spans Marvell’s AI platform portfolio, including network fabrics, co-packaged and near-packaged optics, and emerging memory fabric products.

The engineer will optimize AI serving and training stacks, identify bottlenecks across application, compute, memory, communication, GPU kernel, network, and data/storage layers, and develop credible, reproducible benchmarks relevant to customer workloads. The role includes profiling serving internals and turning technical findings into recommendations and performance narratives. The engineer will collaborate with NRE/SRE and network or fabric architects, and prepare benchmark materials for customers, executive reviews, and trade shows.

The expected base pay range is $186,900–$275,000 per annum. The role is eligible for bonus and equity plans, including a new-hire equity grant and annual equity refreshers. The position may require access to export-controlled technology or software; applicants may be subject to an export license review process. Marvell provides benefits spanning financial well-being, family support, mental and physical health, and recognition, with highlights including an employee stock purchase plan, family support programs, mental health resources, and recognition and service awards.

Requirements

• 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 GPU profiling tools such as Nsight Systems/Compute, PyTorch Profiler, or ROCm Compute Profiler.
• Demonstrated ability to diagnose end-to-end bottlenecks across application, framework, runtime, and infrastructure layers.
• Working knowledge of at least one distributed training framework, such as PyTorch DDP/FSDP, DeepSpeed, or Megatron-LM.
• Strong Python and Linux skills.
• Comfort working in containerized environments such as Docker to ensure benchmark reproducibility.
• Ability to translate deep technical analysis into clear recommendations and concise performance narratives.
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field; the posting also notes a PhD is a plus.
• Applicants must be eligible to access export-controlled information as defined under applicable law; applicants may be subject to an export license review process.

Nice To Haves

• Go experience.
• Kubernetes experience.
• 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 such as GPTQ and SmoothQuant.
• Exposure to customer proofs of concept, competitive benchmarking, or externally consumed performance reporting.
• PhD in a related field.

Responsibilities

• Drive end-to-end performance optimization across AI serving and training stacks using leading models and customer-relevant configurations.
• Identify bottlenecks across the data path, including application request flow, compute, memory, communication layers, GPU kernels, network transport, and data/storage pipelines.
• Develop and execute reproducible, technically credible benchmark methodologies relevant to customer use cases.
• Profile serving-stack internals, including quantization, scheduling, KV-cache management, and disaggregated prefill/decode, to identify optimization targets.
• Analyze results and develop performance narratives to guide kernel, runtime, communication, and system-level optimizations.
• Work 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.
• Run AI benchmarking experiments, operate GPU/HPC lab clusters, and validate Marvell switching and AI fabrics under production-like conditions as part of the AI Solutions Lab’s work.

Benefits

• Bonus and equity plans, including a new-hire equity grant and annual equity refreshers.
• Employee stock purchase plan with a 2-year look back.
• Family support programs to help balance work and home life.
• Mental health resources.
• Recognition and service awards.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level