Chris Ying is a Staff Software Engineer in San Francisco with 12 years of experience building production-grade perception and ML systems for autonomous vehicles, most recently at Waymo after leadership roles at Cruise. He combines deep learning research foundations from Google Brain and CMU with hands-on product delivery—leading machine perception teams at Ambient.ai and shipping LiDAR and semantic understanding pipelines in AV stacks. Chris has contributed to influential open research tooling such as the NASBench API, improving dataset querying and usability for model evaluation, reflecting a mix of research rigor and pragmatic engineering. Known for moving algorithms into scalable data pipelines and training workflows, he thrives at the intersection of research, systems, and applied safety-critical deployment.
12 years of coding experience
7 years of employment as a software developer
Master of Science (MS) Machine Learning, Master of Science (MS) Machine Learning at Carnegie Mellon University
NASBench: A Neural Architecture Search Dataset and Benchmark
Role in this project:
ML Engineer
Contributions:10 commits, 6 PRs, 6 pushes in 11 months
Contributions summary:Chris contributed to the NASBench API, focusing on querying the dataset for model evaluations and incorporating features like "stop_halfway" for partial training results. They modified the API to improve metrics retrieval, adding functionality to access fixed and computed statistics. Furthermore, the user added a Colab notebook demonstrating usage of the NASBench dataset.
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