Lead AI Graph Compiler Engineer

About the role:

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.

LOCATION: Candidates based in the US or Canada  are welcome to apply. You can work in our office in Los Altos, CA or remotely from home. Onboarding for new hires is conducted in-person in our Los Altos, CA office.

What you will do:

• 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.

What you bring to the table:

• 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.

Helpful but not required:

• Experience with ML graph optimizations, parallel / distributed programming, heterogeneous ML computation, and/or code generation.
• Advanced degree in Computer Science or a related area is a plus.

What Modular brings to the table:

• Amazing Team. We are a progressive and agile team with some of the industry’s best engineering and product leaders.
• World-class Benefits. In order to attract the best, we need to offer the best. Premier insurance plans, up to 5% 401k matching, flexible paid time off, and more are available to you! Please note that specific benefit packages may vary based on your location.
• Competitive Compensation. We offer very strong compensation packages, including stock options. We want people to be focused on their best work and believe in tailoring compensation plans to meet the needs of our workforce. 
• Team Building Events. We organize regular team onsites and local meetups in Los Altos, CA as well as different cities. Traveling 2-4 times a year is expected for all roles. 

Working at Modular will enable you to grow quickly as you work alongside incredibly motivated and talented people who have high standards, possess a growth mindset, and a purpose to truly change the world.

The estimated base salary range for this role to be performed in the US, regardless of the state, is $234,000.00 - $286,000.00 USD. 
The estimated base salary range for this role to be performed in Canada, regardless of the province, is $221,000.00 - $280,000.00 CAD.
The salary for the successful applicant will depend on a variety of permissible, non-discriminatory job-related factors, which include but are not limited to education, training, work experience, business needs, or market demands. This range may be modified in the future. The total compensation for a candidate will also include annual target bonus, equity, and benefits, with equity making up a significant portion of your total compensation.

For candidates who fall outside of the listed requirements, we nevertheless encourage you to apply as we may have openings that are lower/higher level than the ones advertised.