I am an ML infrastructure engineer at Google Ads, partnering with researchers to design high-performance workflows, optimize fleet-wide efficiency, and develop agentic tools to improve ML experimentation velocity.

I am also the author of AI for Software Engineers, a publication that gets software engineers hands-on with AI and is read by over 13,000 developers.

You can find me on X and Substack, or chat by shooting me an email.

What I'm working on

Previous Experience

  • ML Infrastructure Engineer at Google, building developer AI tools and improving machine learning velocity.
  • ML Infrastructure Engineer at Microsoft, connecting new compute to training runtimes and managing asset reuse across the fleet.
  • Software Engineering Intern at Microsoft Security Response Center, developing open-source hardware monitoring systems in Rust.
  • Research Assistant at BYU, developing CNN-based MRI semantic segmentation models.

What I Believe

  • Machine learning is about creating excellent products and services people can use and apply. The key to building a great machine learning system is to be a great engineer.
  • The future of AI belongs to engineers. The impact of AI research relies on how well we can usefully put it into the hands of users.
  • I don't understand that which I can't build. Building is the fastest way to turn ideas into real understanding.
  • You can just do things, but don't do too much. It's better to take a step back and ensure you're headed in the right direction.
  • Always be yourself. In work, life, and relationships, everything tends to work out better when you do. Also, everything's more fun with a bit of whimsy.
  • Ask for help. The best way to get better at something is to learn from others. Most people will be willing to help.
  • All software design should be user focused. If it needs to focus on something other than the user to make money, it probably isn't a great product.