I’m an engineer focused on socially impactful problems, with a pragmatic approach to software that balances technical quality with business needs.
- My technical interests are machine learning infrastructure, distributed systems, and cloud-native abstractions that make powerful systems feel intuitive and safe.
Currently, I work at Applied, where my team and I build GPU infrastructure for training and serving autonomous vehicle models. Most recently, I improved throughput for our end-to-end model training architecture by 5x.
Before that, I helped build and scale Cloud Engine, a platform to help companies continuously validate their autonomous vehicles in simulation. I’ve worked on and led several projects. Here are some of my favorites:
- Optimized our results search platform by >99%, enabling users to analyze millions of simulation results in milliseconds instead of minutes. This effort later scaled organization-wide to track and triage costly queries.
- Drove reliability, scale, and decoupling in our CI services, allowing customers to run 20x more simulations per day with zero downtime.
I’ve also worked as a software engineer at Productiv, Amazon, and contributed open-source to Ray (Anyscale).
I graduated from UC Berkeley with a B.A. in Computer Science and a B.A in Data Science. At Cal, I spent much of my time developing software for non-profits, teaching databases, and conducting data systems research.
Outside of work, I enjoy home-cooking, world history, and amateur boxing. View my resumé or shoot me an email at micahtyong@gmail.com.