What you'll be doing:
Create well-designed and well-tested SW systems and PoCs to support recipe exploration for research settings
Analyze and prototype state-of-the-art methods for quantization and sparsity
Benchmark, profile, and optimize LLM workloads in cluster settings
Develop new data analysis tools and visualizations to aid in numerics debugging
Keep developer and researcher productivity and efficiency high by removing obstacles (e.g., slow CI systems, slow training systems)
Participate in code reviews, address code review feedback if applicable.
What we need to see:
An MS or PhD or equivalent experience in Computer Science or a related field, and 5+ years of relevant software engineering experience.
Proficient in Python
Experience with PyTorch or similar framework
Strong Software Engineering background with a focus on building concise and well-tested code
Experience working with ML accelerators, performance optimization and debugging
Strong written and oral communication skills
Ways to stand out from the crowd:
Proficient in precision and numerics for ML
Familiarity with C++ and CUDA
Strongfoundation in LLM pre training, post training, or generation
You will also be eligible for equity and .
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