Systems Performance Engineer, AI Solutions Lab

About Marvell Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities.  At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead.  Your Team, Your Impact The Solutions Architecture team sits within Marvell's Platform organization, which turns advanced silicon into deployable, customer-ready AI and cloud infrastructure. This team — the AI Solutions Lab — owns the validation layer that proves our platforms perform as promised, across compute, networking, and memory fabrics. Without it, Marvell could design great silicon but could never prove, at rack scale, that it performs the way customers expect — and hyperscalers don't buy chips on paper, they buy validated systems. Day to day, the team runs AI benchmarking experiments that mirror real customer workloads, operates GPU/HPC lab clusters, validates Marvell's Teralynx switching and AI fabrics under production-like conditions, and builds live demos for trade shows — carrying platform programs from concept to production. What sets this team apart is breadth of ownership: engineers here aren't limited to validating a single switch chip — they work across the full breadth of Marvell's AI platform portfolio, from Teralynx network fabrics to integrated optical platforms like co-packaged optics (CPO) and near-packaged optics (NPO), as well as emerging memory fabric products such as CXL and photonic fabric memory appliances. This cross-domain ownership — networking, optics, and memory, together — is work that's often siloed across separate teams elsewhere, and the results feed directly into customer proof points and executive reviews. What You Can Expect •     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.

Required Qualifications   •     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), given the role's scope spans both training and inference stacks.  •     Strong Python and Linux skills; Go experience