Senior AI Graph Compiler Engineer

Role Description

The graph compiler is a central piece of MAX, which executes ML models at state-of-the-art performance across multiple hardware platforms. It is built on MLIR from the ground up using a novel design based on tight integration with the Mojo programming language. Modular’s compiler stack is vertically integrated, from ML serving layer, through the Python API, down to highly tuned kernel implementations - opening up opportunities for optimization not available in competing technologies.

This role involves extending and developing the graph compiler to support state-of-the-art features and hardware platforms. You will improve the performance and usability of the graph compiler, creating a joyful developer experience. Modular’s vertically integrated ML serving stack allows you to solve a uniquely diverse set of problems, like heterogeneous compilation, kernel fusions, and distributed model execution.

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 at headquarters in Los Altos, CA.

What you will do:

• Design and develop compiler optimizations to improve inference efficiency across CPUs, GPUs, and ML accelerators, including kernel fusion, memory planning, and shape optimization.
• Compile large-scale application workloads onto heterogeneous hardware: map hardware-agnostic graphs of computational operators onto hardware-specific graphs of devices and nodes.
• Collaborate with the kernels, serving, and Mojo compiler teams to implement core technologies that deliver end-to-end performance across a variety of heterogeneous hardware platforms.
• Write design documents for new features and drive for alignment on designs with cross-functional teams.
• Collaborate with the customer success team and engage with customers to understand their performance requirements and use cases.

Qualifications

• 5+ years of software engineering experience.
• Proficiency in C++.
• Experience working with compilers for machine learning frameworks.
• Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others.

Requirements

• Knowledge of and experience working with MLIR and LLVM (helpful but not required).
• Experience with ML parallel / distributed programming, heterogeneous ML computation, and/or code generation (helpful but not required).
• Knowledge of basic ML implementation and modeling techniques and familiarity with ML frameworks like PyTorch, JAX, or TensorFlow (helpful but not required).

Benefits

• Amazing Team: We are a progressive and agile team with some of the industry’s best engineering and product leaders.
• World-class Benefits: Premier insurance plans, up to 5% 401k matching, flexible paid time off, and more are available to you!
• Competitive Compensation: We offer very strong compensation packages, including stock options.
• 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.

Company Description

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 $198,000.00 - $286,000.00 USD.

The estimated base salary range for this role to be performed in Canada, regardless of the province, is $194,000.00 - $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.