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Snowflake DEEP LEARNING SYSTEM OPTIMIZATION ENGINEER United States, California 267994788
02.08.2024
RESPONSIBILITIES:
- Analyze and optimize GPU kernel performance for deep learning models.
- 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.
- Build a strong system foundation for Snowflake Arctic by interacting with model scientists for model-system co-development.
- Stay updated with the latest advancements in GPU kernel optimization, deep learning, and LLM system development.
- Publish their innovations, optimizations, and engineering practices in technical blogs, top-tier conferences and journals.
- Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. A Master’s degree or PhD is preferred.
- 5 years of experience in GPU kernel optimization, deep learning system optimization, or high-performance computing (HPC).
- Proficiency in programming languages such as C/C++ and Python.
- Strong understanding of GPU architectures and experience with CUDA or similar frameworks.
- Experience with profiling tools (e.g., nvprof, Nsight) and performance analysis methodologies.
- Solid problem-solving skills and ability to debug complex performance issues.
- Experience with version control systems (e.g., Git) and collaborative development practices.
- Excellent communication skills and ability to work effectively in a cross-functional team environment.
The following represents the expected range of compensation for this role:
- The estimated base salary range for this role is $214,000 - $327,750.
- Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan.
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