Staff Engineer, Inference Optimizations

🕒 October 31

⛰️ Colorado – Remote

⛰️ Colorado – Remote

💵 $191.2k - $239k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

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📋 Description

• Lead technical strategy for benchmarking and performance optimization at inference-engine and GPU-kernel layers • Engineer solutions for attention-layer optimization, memory and precision management, and parallelization across multi-node GPU clusters • Implement cutting-edge optimization techniques for generative AI inference • Improve batch-size performance using AMD AITER, including tuning CK or ASK for FP8/BF16 on AMD MI355X • Identify kernel-fusion opportunities for GLM-5 Transformer-block kernels, including FlashAttention and RMS Norm • Tune expert gateway router kernels for MoE models such as Qwen3-235B, DeepSeek V3, and GLM-5 • Advise on modern NVIDIA and AMD GPU hardware procurement and software integration • Develop and deploy quantization techniques including FP8, INT8, and FP4 • Lead code and design reviews and mentor engineers without direct administrative management • Partner with Product Management and TPMs to turn hardware limits into shippable product features • Contribute to and integrate open-source AI while participating 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 (LLM, VLM, LMM) landscape • 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; experience contributing to the Triton compiler or writing custom CUDA kernels for a major LLM desired • Expert knowledge of inference optimization, including attention layers, memory and precision management, and distributed GPU parallelization

🏖️ Benefits

• Professional development resources • 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 • Potential new hire equity grant • Employee Stock Purchase Program opportunity in participating countries • Equal-opportunity employment

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