Samuel Ginzburg is a research-focused software engineer with 11 years of experience building high-performance systems for ML frameworks, compilers, and OS-level platforms. He has contributed to Triton and PyTorch at Meta and developed distributed automatic partitioning, GSPMD/XLA support, and NCCL integrations during a machine learning compiler internship at NVIDIA. His background spans kernel and systems work—porting SGX to ESXi and optimizing Windows kernel heaps—alongside cloud and serverless research at Microsoft, reflecting deep expertise across hardware-adjacent and distributed software stacks. A PhD candidate in Computer Science at Princeton with a strong undergraduate foundation, he pairs rigorous research methods with practical implementation skills in C, Python, and compiler toolchains. Notably, he bridges low-level systems and cutting-edge ML infrastructure, enabling efficient execution of large-scale models. Based in New York, he brings a rare combination of kernel-level engineering and ML compiler research to problems that require both performance tuning and architectural design.
11 years of coding experience
2 years of employment as a software developer
Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at University of Massachusetts Amherst
Computer Science, Computer Science at University of Connecticut
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Princeton University
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