Soham Govande is a Member of Technical Staff specializing in high-performance computing, GPU optimization, and AI kernel development, with eight years of hands-on experience across leading AI and hardware organizations. Based in Palo Alto, he has contributed to low-latency ML inference research at Stanford SAIL (work spotlighted at ICML2025/MLSys2025) and shipped infrastructure and model-acceleration work at OpenAI and NVIDIA. His portfolio blends production-grade systems engineering with ML compiler know-how, focusing on squeezing maximum performance from modern accelerators. Soham’s public-facing projects and writing (sohamgovande.com) reflect a curiosity for experimental, performance-critical tooling—he also summarizes his GitHub work as “fun stuff @ openai,” hinting at exploratory open-source contributions. Comfortable moving between research and product delivery, he excels at turning algorithmic ideas into optimized kernels that run in the real world.
8 years of coding experience
1 year of employment as a software developer
High School Diploma, High School Diploma at Round Rock High School
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Stanford University
Contributions:6 releases, 539 commits, 1 PR in 3 years 2 months
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