AI System Research and Development Engineer - Optimization
Develop and optimize GPU kernels and deep learning systems for LLM training and inference at Snowflake AI Research. Requires 5+ years in GPU/HPC optimization and strong proficiency in PyTorch, TensorFlow, JAX, and CUDA.
200k – 265k
On-site5+ YOEML Engineering
About the role
Responsibilities
Analyze and optimize GPU kernel performance for training and inference of LLMs
Develop and implement strategies to enhance the efficiency and scalability of deep learning systems
Profile and benchmark deep learning systems using tools and techniques to identify bottlenecks
Design and implement optimizations to reduce latency and improve resource utilization for training and inference
Stay updated with the latest advancements in GPU kernel optimization, deep learning, and LLM system development
Contribute to the development of agentic frameworks and applications for LLM-driven workflows, enhancing automation, reasoning, and decision-making capabilities
Open-source and publish innovations, optimizations, and engineering practices in technical blogs, top-tier conferences and journals
Requirements
Bachelor’s degree in Computer Science, Electrical Engineering, or a related field (Master’s degree or PhD preferred)
5 years of experience in GPU kernel optimization, deep learning system optimization, or high-performance computing (HPC)
Proficiency in deep learning frameworks such as PyTorch, TensorFlow, JAX
Strong understanding of GPU architectures and experience with CUDA or similar frameworks
Experience with frameworks like CUTLASS, Triton, cuDNN, etc.
Experience with profiling tools (e.g., nvprof, Nsight) and performance analysis methodologies
Solid problem-solving skills and ability to debug complex performance issues
Excellent communication skills and ability to work effectively in a cross-functional team environment
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