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Modular

AI Framework Engineer, Graph Modeling

US Canada
USD 187k - 286k
Machine Learning API PyTorch C++
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Description

About Modular

We believe that AI is a net positive force in the world. Our vision and mission are to help rebuild AI infrastructure to advance humanity and our environment. We will do whatever it takes to empower our customers, team, and company to benefit from that pursuit. You can read about our culture and careers here to understand how we work and what we value.

We are owners and advocates for the underlying technologies, developer platforms, product components, and infrastructure. These essential building blocks form the high-quality and coherent experiences our users expect. We aim to drive the pace of innovation for every AI/ML developer.

About the role:

In the Max Engine SDKs team, we are focused on building software that helps drive Modular product adoption by delivering differentiated solutions to the industry’s most challenging AI deployment problems. We work closely with compiler, performance, and kernel teams to orchestrate core technologies into rich user-facing libraries and tools that facilitate testing, optimization, and deployment of AI models at scale.

This role is for an engineer with an ML modeling background who will help us mature our examples and “batteries-included” offerings along with helping to design and build out our core SDK.

We are looking for candidates based on their breadth and depth of experience in software engineering, machine learning, compilers, hardware, and more. If this sounds exciting, we invite you to join our world-leading AI infrastructure team and help drive our industry forward!

 

LOCATION: Candidates based in the US or Canada are welcome to apply. You can work remotely from home.

 

What you will do:

  • Work with Product and partner engineering teams to design and ship new SDK features
  • Build serving-optimized LLMs and broader modeling libraries on top of our Max Graph API
  • Contribute to and maintain our user-facing MAX benchmarking, development, and visualization tools and inference APIs

 

What you bring to the table:

  • Experience building ML models in PyTorch and/or ONNX
  • Familiarity with HuggingFace API and workflows for using community models
  • Ability to create durable, reusable software tools and libraries that are leveraged across teams and functions
  • Experience in machine learning technologies and use cases
  • Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others, and alignment with our culture
  • Strongly identifies with our core company cultural values.

 

Helpful but not required:

  • In-depth knowledge of modern C++
  • Some experience with and interest in Mojo, our AI programming language!

 

 

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 is $187,000.00 - $270,500.00 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. 

 

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.
  • Flexible Location. We want you to work where you are most productive and we are a remote-first company.
  • World-class Benefits. In order to attract the best, we need to offer the best. Premier insurance plans, up to 5% 401k matching, very generous WFH setup stipends, 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 different cities.

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.

Modular is proud to emphasize an equal opportunity, safe environment for people to do their best work. Modular is an affirmative action employer. We are committed to providing equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you require reasonable accommodations to participate in the interview process, please let your recruiter know, and we will work with you to meet your needs in compliance with the ADA.

This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. If E-Verify cannot confirm that you are authorized to work, this employer is required to give you written instructions and an opportunity to contact Department of Homeland Security (DHS) or Social Security Administration (SSA) so you can begin to resolve the issue before the employer can take any action against you, including terminating your employment. Employers can only use E-Verify once you have accepted a job offer and completed the Form I-9.

Modular
Modular
Artificial Intelligence (AI) Machine Learning Software

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