Meet the team building the future of AI for the world

Distinguished Leader who founded and scaled critical infrastructure including LLVM, Clang, MLIR, Cloud TPUs and the Swift programming language. Chris built AI and core systems at multiple world leading technology companies including Apple, Google, SiFive and Tesla.

Repeat Entrepreneur and Product Leader. Tim helped build, found and scale large parts of Google's ML infrastructure at Google Brain and Core Systems from APIs (TensorFlow), Compilers (XLA & MLIR) and runtimes for server (CPU/GPU/TPU) and TF Lite (Mobile/Micro/Web), Android ML & NNAPI, large model infrastructure & OSS for billions of users and devices. Loves running and building for the world.

Engineering leader with 15+ years of experience building, shipping, and operating AI/ML, HPC, distributed computing frameworks, and cloud platforms, at the scale of millions of users and thousands of enterprises. Grew teams and scaled the design and implementation of deep learning frameworks, personalized speech recognition, and conversational AI services at Microsoft. Amit also scaled and optimized speech and computer vision runtimes, including GPU acceleration and ASICs. Amit enjoys hiking, playing billiards, and exploring the world with his family in his free time.

Product leader who has built and scaled AI applications and infrastructure. Eric led the TensorFlow API, Compiler, and Runtime teams at Google ML, including the founding of TFRT and the productionization of JAX. He holds an MBA from Wharton and Computer Science MS from Penn and loves soccer, fitness, and the great outdoors.

Mike has spent over 25 years working in the fields of IT, corporate operations, and software development - most recently at Apple. Mike volunteers his time serving as a Board member with the LLVM Foundation, focusing on finance and operations. Mike truly believes in the power of AI to help address some of the world’s greatest needs.

Mrinal is a versatile engineer and leader, with experience working across all parts of the semiconductor industry (architecture, kernels & compilers, applications, fab etc). Prior to joining Modular, he spent 6 years working for Deep Learning ASIC startups (Graphcore and Nervana) and brings in a wealth of knowledge on full stack performance of AI applications, starting from the models to frameworks, compilers, runtimes and assembly. At Graphcore, he led the effort to create highly performant and scale-out training implementations of BERT & Resnet for MLPerf. He enjoys spending his free time with family, trail running and cooking.

Software Engineering lead with over 15 years of experience working at Google, Microsoft and a handful of startups. Nick has contributed to many technologies in Machine Learning such as TensorFlow.js, TensorFlow Lite/Micro, and ONNX/ONNXRuntime. Nick enjoys spending his free time with family and enjoying the Minnesotan outdoors.

Leader in compilers, programming models, and AI systems. Before joining Modular, Tatiana led CPU and GPU compiler infrastructure for Google ML, co-founded MLIR, and was a Principal Engineer and Director of Programming Systems Research at Intel Labs. Tatiana has published ~40 research papers, holds ~50 US and international patents, and has served on numerous research conference programming committees, including being Co-Chair of CGO 2022.

Expert in machine learning, compilers, programming languages, and accelerated computing. Before Modular, Abdul led the development of AI compilers for GPUs at Microsoft Research and the Mathematica Compiler at Wolfram Research. Abdul has developed open-source tools for accelerating real-world applications to optimize their performance across the hardware and software stack.

Aman specializes in building and deploying complex technologies on a variety of platforms. He has, and continues to design and build cutting-edge compiler and runtime stacks for novel accelerators, heterogeneous SoCs, and traditional server deployments at companies like Apple and SambaNova Systems. Aman cares deeply about democratizing state of the art technology.

Arjun specializes in the intersection of Machine Learning and Software Engineering. Prior to joining Modular he was a Machine Learning Engineer on Adobe's On-Device Machine Learning team. Arjun is passionate about making things simpler and accessible to a wider audience, and enjoys tinkering with system level software and learning about programming languages internals.

Chad has a strong background in low-level code optimization, parallel programming, and high performance computing. He has worked for more than 17 years in research and engineering in Europe and North America. He holds a PhD in particle physics and is one of the authors of the Higgs Boson discovery. Chad has a passion and focus for understanding things at a fundamental level. Prior to joining Modular he worked at Graphcore working closely with the hardware engineers to implement and optimize custom features of the GC hardware, and before that Simula Research Laboratory among many other distinguished research facilities. He enjoys spending his spare time with his family, travelling, collecting rare coins, and eating excellent food.

Dan has over 15 years of software engineering experience in a wide variety of fields and roles. Before Modular, Dan worked in teams such as Google Brain and TensorFlow, where he co-authored AutoGraph, led improvements to system architecture, graph representations and APIs, developed ML and TensorFlow training material, and collaborated with ML researchers. Dan is passionate about developing next generation programming languages for machine learning. Dan enjoys hiking, stargazing and traveling around the world.

Working on web infrastructure since AWS offered only 2 services (S3 & EC2). Past stints include automating global capacity management and improving maps reliability at Uber and helping scale Heath from $0 to $1 million ARR. Likes Robert Caro, death metal, Magic the Gathering, and deterministic builds.

James is passionate about building tools that boost programmer productivity, focusing on compilers for heterogeneous computing and AI acceleration while at Purdue, Stanford, Google, and SambaNova Systems. James believes deeply that technology, and AI in particular, must be fair to be beneficial, which necessitates that it is easy to use and understand.

Compiler Engineer, MLIR Developer, and former Robotics Hacker. Jeff studied Mechatronics Engineering at the University of Waterloo where he explored his passion for Robotics before falling in love with MLIR and Compilers while at Google, where he worked making TensorFlow faster and better for everyone. Jeff is now developing MLIR-based compilers at Modular to make AI better for the World.

Expert in C++ and low-latency programming. Before Modular, Joe was at Quantlab writing cutting-edge C++ generic libraries for real-time trading systems that push the boundaries of performance for software and hardware. In his free time, Joe enjoys traveling, hiking, and spending time with his fiancé and dog.

Expert in high-performance computing, parallel computing, concurrency models, runtime systems and accelerating big data analytics. Before Modular, Juan led parallelization and performance development of Bodo.ai's compute and query execution engine. Juan was a core developer of Charm++ at UIUC, a production parallel programming framework used by supercomputing applications that scale to millions of cores, and is the author of Charm4py.

Kate is a machine learning engineer passionate about optimizing AI deployment and broadening the impact of developments in the field. Before Modular, Kate worked as as a Machine Learning Research Apprentice while studying for her MS in Computer Science at Northeastern University. In her free time, Kate likes book clubs, cooking, and feeding her dog treats.

Laszlo is a former data scientist turned software engineer. Before Modular, he built XMOS’s first ML compiler and maintained their TFLite Micro runtime, then worked as lead engineer on the MLIR-based software stack at Luminous Computing. He enjoys traveling and all New England seasons.

Liina is an experienced talent acquisition specialist and leader who most recently built and shaped the global TA process at SiFive. With previous work experience in Japan, the US and Europe, Liina has joined Modular to build a global and world-class team who will reinvent the AI infrastructure for the world.

Lily is a machine learning compiler engineer with a passion for programming languages. In her daily work, she likes solving engineering design problems elegantly and practically. Before Modular, Lily worked on Apache TVM (an open source machine learning compiler) at OctoML. In her free time, Lily loves being outdoors— hiking, biking and gardening. She also enjoys woodworking and ceramics.

Luca has over 10 years of experience in compilers and tools, powering Fortune 500 companies and startups like SambaNova Systems. He enjoys working on all aspects of programming language development, whether it is the IDE, the core compiler, the official specification or the design of a new language feature. Luca is passionate about finding simple solutions with the right abstractions to solve complex problems.When not working, he likes to cook Italian dishes for his family and travel.

Mark’s engineering career has spanned programming languages and compilers, maps and transport routing, streaming dataflow and, most recently, deep learning compilers. He joins Modular after most recently working on Apache TVM, and before that a distinguished career at Google. Mark is now looking forward to radically reducing the accidental complexity in model compilation, deployment and monitoring.

Mark lives with his family in Seattle and is a keen back country hiker, trail runner, climber and renovator.

Software Engineering lead with a strong background in mathematics, optimization, and team management. Matthew comes to Modular with over 7 years of experience working on AI models and Compilers at large companies like Intel and small startups working on technologies like TVM. He's a total coding nerd, and loves picking up new programming languages and tracking down bugs.  Matthew enjoys spending his free time hiking in the Wasatch Mountains, and loves to bake delicious breads and pizzas for his family.

Expert in traditional and ML compilers with over 15 years of experience in the field. Before Modular, Michael contributed to Intel Compiler, GCC, LLVM, and most recently PyTorch. He is passionate about building right things the right way and excited to do that at Modular to help power AI for the world.

Paige has spent several years building an impressive set of leadership, logistic, and operational skills in the industries of executive concierge management and outdoor adventure experiences. Paige joined Modular with the vision and aspiration to help AI change the world for the better.

Software Architect, Compiler Engineer, and Programming Language enthusiast. River is a leader within MLIR, and has driven core developments since its inception. Before joining Modular, he developed and evangelized MLIR within Google ML, working with TensorFlow, JAX, Google Tensor, and more. River is passionate about his family, staying fit, and crafting cutting edge developer technology.

Product manager with a long experience designing and delivering ML experiences and infrastructure at scale. Prior to joining Modular, Rosane led the Windows ML and DirectML product teams at Microsoft. She holds a PhD in Machine Learning and loves traveling, reading and spending time with her family and dog.

Shiming is an enthusiast on cutting edge AI hardwares and automatic low-level code generation to enable accessibility and optimality on a broad spectrum of hardware platforms after working for years at Nvidia, and on Pytorch and AI. Shiming has worked on fast GPU kernel generation, as well as building commercial grade edge and server hardwares. He deeply believes in achieving maximal utility on every bit of silicon fabricated and making every joule spent on compute count. In his spare time, he enjoys spending time with family and reading up on technology.

Software engineer specializing in compilers and runtimes. Before Modular, Taewook tech-led multiple compiler teams at Meta and Samsung, from a LLVM compiler team for server-side applications to a team that builds a compiler for hardware accelerators. He has served the compiler research community as a conference committee member of CGO and LCTES.

Tyler is a performance optimization & hardware acceleration specialist. He holds bachelor’s and master’s degrees in computer engineering from Lehigh University. Prior to Modular, Tyler developed compilers, simulators, instruction sets and kernel libraries for AI accelerators first at IBM and then at Lightmatter. He is passionate about forecasting technology and understanding, to the best of his ability, the long-term impacts of artificial intelligence. On weekends Tyler enjoys anything and everything outside, particularly running, camping, playing ultimate frisbee, boating/wakeboarding and skiing.