As a high level summary, I am a well-known leader who is distinguished by a hands-on approach to building systems.

• I am co-founder and CEO of Modular AI.
• I built and scaled many open source compiler infrastructures, including LLVM, Clang, MLIR, and CIRCT. These span the gamut of fundamental systems at the HW/SW boundary - powering iOS, Android, CUDA, TensorFlow, and countless other products and technologies. This includes thousands of engineers collaborating globally on shared code, redefing a generation of compiler technology.
• I built, managed, and launched the Swift programming language which is used by millions of iOS and other programmers worldwide. Swift popularized a wide range of academic ideas in a mainstream language (including a nice concurrency model).
• I helped scale ML systems globally bringing Google Cloud TPUs to market, unifying the ML compiler stack at Google (and beyond) with MLIR, scaling Google's SW and HW on both server and edge to impact billions of users.
• I brought engineering and product focus to SiFive, defining and launching the SiFive Intelligence product line, and scaled the global engineering team.
• I contributed to many other commercial and open source projects at Apple, Tesla and Google, and SiFive.

For more information, please see:

1. I am @clattner_llvm on Twitter, lattner on GitHub, and am on LinkedIn.
2. My resumé page contains:
   • My Work history.
   • Articles, blogs, and proposals I've written.
   • Slides and recordings from talks, podcasts, and other presentations.
3. I am a woodworker and have a few project pictures online.
4. I am not a web designer.

In January 2022, Tim Davis and I launched Modular AI to rebuild global ML infrastructure from the bottom up. I lead the team as CEO.

From January 2020 to January 2022, I led the Engineering and Product teams (incl. Hardware, Software, and Platform Engineering) at SiFive. SiFive builds the world's best RISC-V CPU and other IPs, a wide range of software for RISC-V, and an chip design methodology that accelerates HW design.

From August 2017 to January 2020, I built a number of incredibly cool things at Google in the TensorFlow team, and led the infrastructure efforts, including hardware support (CPU, GPU, TPU), low-level runtime, and language efforts. Details are in my Google Resumé.

In early 2017, I briefly ran the Tesla Autopilot team. Details are in my Tesla Resumé.

I worked for Apple from July 2005 to January 2017, holding a number of different positions over the years (a partial history is available in Apple resumé). This included managing the Developer Tools department, which was responsible for Swift Playgrounds for the iPad, Xcode, and Instruments, as well as compilers, debuggers, and related tools. I remained a member of the Swift Core Team until 2021.

I created and built the MLIR Compiler Infrastructure with a small-but-amazing team of compiler engineers at Google, starting from a whitepaper in April 2018. The TensorFlow product (and machine learning in general) needs compilers of many different kinds for a wide range of graph-level problems, accelerators of all kinds, and a lot of 'glue' tools to tie everything together. Because existing solutions existed, we took time to build a world-class system, getting the core abstractions right, and investing in building great infrastructure and a team culture that valued engineering excellence.

The MLIR project started as a scrappy but ambitious effort to reimagine how the entire stack worked, and produced a really interesting and novel approach to constructing compilers of all kinds. "MLIR" stands for "Multi-Level Intermediate Representation": it is a profound approach to compiler design, enabling a new generation of algorithms, approaches, and applications to many related tooling technologies. It is such a new approach that I believe it will take multiple years of further work for the compiler community to digest the implications, adapt, and exploit the new capabilities.

While MLIR is young it has seen rapid and widespread adoption and deployment within Google and rumored to be widely used across industry, including SiFive, Apple, Intel, Microsoft, Nvidia, ARM, and many more for many projects (including production deployments saving large amount of compute and OpEx dollars). MLIR is part of the LLVM umbrella project in the mono-repo - building on the widely accepted reputation of the LLVM community - and is used by other LLVM projects like the Flang Fortran compiler, the CIRCT project, and others.

To learn more about MLIR, I'd recommend reading the canonical paper published about it. Please consider citing it if you build interesting work using it. The MLIR web site has many other talks and publications as well.

I started work on the Swift Programming Language (wikipedia) in July of 2010. I implemented much of the basic language structure in secrecy: A few other folks started contributing in earnest late in 2011, and it became a major focus for the Apple Developer Tools group in July 2013.

The Swift language is the product of tireless effort from a team of language experts, documentation gurus, compiler optimization ninjas, and an incredibly important internal dogfooding group who provided feedback to help refine and battle-test ideas. Of course, it also greatly benefited from the experiences hard-won by many other languages in the field, drawing ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and far too many others to list. As of Dec 2015, Swift is open source, and you can participate at http://swift.org.

The Xcode Playgrounds feature and REPL were a personal passion of mine, to make programming more interactive and approachable. The Xcode and LLDB teams have done a phenomenal job turning crazy ideas into something truly great. Playgrounds were heavily influenced by Bret Victor's ideas, by Light Table and by many other interactive systems.

I hope that by making programming more approachable and fun, we'll appeal to the next generation of programmers and to help redefine how Computer Science is taught. Swift Playgrounds for the iPad is a crucial piece of this: it uses real Swift code to teach programming fundamentals, while also providing a full featured native iOS development experience that allows access to almost the entire iOS SDK. Swift Playgrounds includes high production value assets and curriculum produced by Apple, which provides a appealing and safe introduction to programming concepts for everyone.

To learn more about the origins of Swift, I recommend listening/reading my thoughts in various interviews: January 2017 on the ATP Podcast (transcript), January 2019 on the Swift Community Podcast (transcript excerpt), or July 2021 on Swift by Sundell.

I am the original author of the LLVM Compiler Infrastructure, an open source umbrella project that includes all sorts of toolchain related technology: compilers, debuggers, JIT systems, optimizers, static analysis systems, etc. I started both LLVM and Clang and am still the individual with the most commits. Of course, as the community has grown, my contribution is being dwarfed by those from a wide range of really amazing folks.

LLVM has enjoyed broad industry success - being widely used in commercial products - as well supporting hundreds of academic papers. LLVM is now overseen by the LLVM Foundation a 501(c)(3) nonprofit, and I serve on its Board of Directors. For its contribution to the software industry, LLVM has been recognized with the ACM Software System Award.

For more details about LLVM, see:

1. LLVM Compiler Infrastructure home page
2. Invited talks about LLVM and other topics
3. Random notes on LLVM - Unofficial notes and thoughts on LLVM extensions and todo items.
   

Over the years, I've written a lot of things, delivered many public talks, and have published some academic papers.