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 founder and author of AI for Software Engineers, a publication dedicated to helping developers learn deep AI with a focus on infra, ops, and engineering, 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
- • Co-developing an MLE Agent with Google DeepMind to automate ML experimentation by integrating ML context into Antigravity.
- • Publishing AI for Software Engineers (see above).
- • Developing ML Basics and Logan's Guide to organize the best resources to learn AI.
Previous Experience
- • ML Infrastructure Engineer at Google, building developer AI tools and improving machine learning velocity.
- • Machine Learning Infrastructure Engineer at Microsoft Azure, 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
- • 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.
- • 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.