Lead AI Graph Compiler Engineer, Modular

• 5+ years of compiler engineering experience
• A track record of challenging the status quo and delivering significant, measurable improvements
• Experience working with compilers for machine learning frameworks, such as PyTorch
• Knowledge of core compiler algorithms and data structures
• Knowledge of and experience working with MLIR and LLVM
• Knowledge of C++, as well as, knowledge of basic GitHub workflows like pull requests
• Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others, and alignment with our culture

Desirable

• The Modular graph compiler is a central piece of the MAX Framework which executes ML models at state-of-the-art performance across multiple hardware platforms
• It uses a novel design based on integration with the Mojo programming language, provides best-in-class features (e.g. Full parametric dynamic shapes), and uses advanced MLIR implementation techniques
• Modular’s compiler stack is vertically integrated, from the API down to the kernel implementation - opening up opportunities for optimization not available in competing technologies
• This role involves extending and developing Modular’s novel graph compiler to support more features and hardwares
• We’re looking for an ambitious engineer who is willing to grow their skills and be a technical lead to an incredible team
• You will set strategic technical direction, contribute to the design and implementation of graph compiler features, and communicate with both leadership and users
• Set the technical direction for a team building the best heterogenous multi-device compiler and runtime system available, using MLIR and LLVM technologies
• Collaborate with the Mojo compiler team to influence and harness Mojo's powerful compile-time meta-programming capabilities
• Develop and disseminate deep expertise in new hardware platforms, partnering with Mojo kernel developers to achieve state-of-the-art performance on the latest hardware
• Tackle complex technical challenges to empower ML engineers and accelerate model development
• Strategically identify technical opportunities to grow the business
• Mentor and develop junior engineers, growing the capability of the team over time