Evan Cheshire is a software engineer with 10 years of experience building high-performance compilers and systems for networking and ML hardware. A Stanford EE master’s graduate, he has driven backend compiler work at Barefoot Networks and currently optimizes latency and power on Apple’s Neural Engine through scheduling, resource allocation, and graph-level memory reduction. He blends low-level C/C++ expertise with higher-level Java and Python fluency to deliver practical optimizations that fit real silicon constraints. His open-source contributions to the P4 reference compiler improved type handling, determinism, and test coverage for the widely used Tofino backend, reflecting a focus on correctness and hardware-aware optimizations. Prior roles at Zoox and earlier internships show a history of shipping robust middleware and instrumentation tools for complex, safety- and performance-sensitive systems. Colleagues rely on him for deep systems thinking that translates into measurable latency, power, and throughput improvements.
10 years of coding experience
6 years of employment as a software developer
Bachelor's Degree, Electrical and Electronics Engineering, 3.77, Bachelor's Degree, Electrical and Electronics Engineering, 3.77 at University of Michigan
Master’s Degree, Electrical and Electronics Engineering, Master’s Degree, Electrical and Electronics Engineering at Stanford University
Contributions:20 commits, 34 PRs, 29 pushes in 2 years 8 months
Contributions summary:Evan primarily contributed to the P4_16 reference compiler, focusing on improving the type information handling for action selectors and profiles within the Tofino backend. They added annotations for counters with min/max width and implemented new test cases. The user also made several bug fixes and optimizations, including fixing a non-determinism issue in a switch profile and adding a bitvec barrel shift function. These changes indicate a focus on improving the compiler's functionality and test coverage.
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