Summary
Michael Kuchnik is a Research Scientist specializing in systems for machine learning, with 11 years of experience designing high-performance ML data pipelines that prioritize both speed and developer ergonomics. His PhD work at Carnegie Mellon and subsequent roles at Meta and Google blend systems research, data representation, augmentation, and model validation into practical tooling—he contributed to Plumber, a tf.data profiling and diagnosis system. Comfortable running large-scale experiments across HPC and cloud environments, he has applied ML to storage, logs, and ETL workloads and brought systems-thinking to model training bottlenecks. Based in Boston, he combines deep academic rigor with hands-on production experience, often surfacing tuning opportunities that are easy to miss during typical ML development.
11 years of coding experience
10 years of employment as a software developer
Bachelor's Degree Computer Engineering, Bachelor's Degree Computer Engineering at Georgia Institute of Technology
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Carnegie Mellon University