Staff Engineer, Inference Optimizations

🔥 14 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $191.2k - $239k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

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📋 Description

• Lead the technical strategy for benchmarking and performance optimization at inference-engine and GPU-kernel layers • Solve complex performance issues involving attention layers, memory and precision management, and parallelization across multi-node GPU clusters • Implement optimization techniques for generative AI inference • Improve batch-size performance using AMD's AITER library and tune CK or ASK kernels for FP8/BF16 • Identify kernel-fusion opportunities for Transformer-block layers such as FlashAttention and RMS Norm • Tune expert-gateway router kernels for MoE models including Qwen3-235B, DeepSeek V3, and GLM-5 • Advise on NVIDIA/AMD hardware procurement and software integration • Develop and deploy quantization techniques including FP8, INT8, and FP4 • Lead code and design reviews and elevate the team's technical standards • Partner with Product Management and TPMs to turn hardware limits into shippable product features • Contribute to and integrate open-source AI projects and maintain a presence in GPU infrastructure and model-performance communities

🎯 Requirements

• 5+ years of experience in high-performance computing or AI infrastructure • Proven track record of solving compute utilization and memory bandwidth bottlenecks • Deep familiarity with the Gen AI landscape, including LLM, VLM, and LMM model families • Hands-on experience with attention-layer optimizations and parallelization across distributed GPU environments • Comprehensive understanding of NVIDIA and AMD GPU architectures and software ecosystems, including CUDA and ROCm • Extensive experience integrating, building with, and contributing to open-source software projects • Excellent system design skills related to low-level GPU programming, optimization, memory access patterns, and parallel execution • Experience acting as a technical lead and driving design and delivery through cross-functional alignment • Deep understanding of GPU architectures, including SMs, warp scheduling, and Tensor Cores • Expert-level Triton or CUDA proficiency • Experience contributing to the Triton compiler or writing custom CUDA kernels for a major LLM is desired

🏖️ Benefits

• Reimbursement for relevant conferences, training, and education, subject to company policy • Flexible time off • Local team meetups • Mental health and well-being support • Annual bonus plan eligibility for non-sales roles • Eligible employees may receive equity compensation, including a new hire equity grant • Employee Stock Purchase Program in participating countries • Professional development resources • Equal-opportunity employment

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