Emilio Andere
| Emilio Andere | |
| Occupation | Entrepreneur, software engineer |
|---|---|
| Known for | Co-founder and CEO of Wafer |
Emilio Andere is a technology entrepreneur and the co-founder and chief executive officer of Wafer, a Y Combinator-backed startup that develops artificial intelligence tools for GPU performance optimization. The company, based in San Francisco, describes its product as "AI that makes AI fast," focusing on profiling, diagnosing, and optimizing GPU code across kernels, models, runtimes, and inference pipelines.[1]
Career
Andere co-founded Wafer to address inefficiencies in GPU kernel development. He has described the company's product as analogous to "Cursor for GPU kernels," noting that while front-end and back-end software engineering have seen significant productivity gains from AI-assisted coding tools, similar improvements have not yet been fully realized for engineers writing GPU kernels. According to Andere, the tool is designed both to assist developers in writing more efficient kernel code and to directly optimize existing AI models.[2]
Wafer's platform is used by engineers at a range of technology companies and institutions, including Intel, LinkedIn, Red Hat, Pinterest, Nuro, Datadog, Naver, and the Massachusetts Institute of Technology.[3]
The company has participated in Y Combinator's accelerator program and has posted job listings for technical staff positions in San Francisco with compensation ranging from $150,000 to $250,000 in salary plus equity.
Andere is active in the GPU computing and AI hardware community. He regularly publishes technical commentary on topics including CUDA programming, NVIDIA Blackwell architecture, mixture of experts inference, heterogeneous computing architectures, and AI chip design. In March 2026, he attended the NVIDIA GTC conference, where he expressed interest in Blackwell kernels, CUDA Tile, and heterogeneous architectures.
The Wafer team is described as small, with Andere noting that the company divides its time between developing the optimization tool itself and using it to improve open-source AI models, including submitting pull requests to external projects.
References