• As the Engineering Manager of the MAX Kernel team, you will lead a team of kernel engineers, own the kernel roadmap and the per-model × per-hardware performance optimization to the best vendor and open-source stacks
• You will partner closely with the Framework, Serve, Compiler/Runtime and Hardware Enablement teams, and help grow a kernel engineering community that spans Modular and Qualcomm
• Lead, hire, coach and grow a team of ~12 kernel engineers across NVIDIA, AMD and ASIC hardwares; own career development, performance management and team health
• Own the MAX kernel roadmap and delivery: match or beat the performance of other open-source kernel libraries on each business essential model × hardware target
• Support portable kernel architecture (TileTensor / TensorEngine / TileIO, reusable building blocks such as MegaFFN and the attention family) together with tech leads
• Partner cross-functionally with Framework, Serve, Compiler/Runtime, Hardware Enablement and Qualcomm kernel teams on priorities, interfaces and escalations
• Advance AI-assisted kernel development (kernel agents, fuzz verification, playbooks) and the kernel engineering community across org boundaries

Benefits

• A variety of fantastic health benefits (health, dental, vision insurance; life insurance etc) are available
• A 401k plan with up to 5% match
• Free tax advice on Carta
• Generous work-from-home stipend of $1500 to help you improve your home office
• Unlimited paid time off and flexible work hours

• Deep understanding of profiling, benchmarking, and roofline analysis
• Track record of hiring, retaining and growing senior kernel or performance engineers
• Strong cross-functional communication; able to drive technical decisions with tech leads and communicate status, risks and tradeoffs to engineering leadership and customers
• 3+ years as an engineering manager leading GPU kernel, compiler, HPC, or ML performance engineering teams. (minimum requirement)
• Working knowledge of writing and optimizing GPU/accelerator kernels (CUDA, HIP/ROCm, Triton, CUTLASS/CuTe or similar)
• Open-source contributions to kernel libraries or inference engines
• Experience with AI-assisted or agentic kernel development
• Multi-target experience beyond NVIDIA: AMD, NPUs/ASICs or edge/on-device
• Experience with Mojo or other kernel and tile-level programming models (Triton, TileLang, CuTe)