Backend Engineer, Cloud Inference

About the role:

In the Cloud Inference team, we are focused on building end to end distributed LLM inference deployments and a repeatable, observable, productive, low toil platform for managing these deployments.

Our goal is to make inference both the fastest and most scalable while also building an easiest platform for deploying and scaling models for enterprises and developers alike. We're seeking engineers who are passionate about pushing the boundaries of distributed inference systems and enjoy working at the intersection of large-scale systems and machine learning. We are looking for candidates based on their breadth and depth of experience in backend engineering, AI inference, and distributed systems development. 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 out of our office in Los Altos, CA or remotely from home. To support growth and collaboration, those in earlier career stages work in a hybrid capacity at our Los Altos, CA office (minimum 2 days per week on-site) with relocation assistance provided for out-of-state candidates. Senior members have both in office or remote flexibility. Onboarding for new hires is conducted in-person in our Los Altos, CA office.

What you will do:

• Build the multi-cloud, multi-tenant platform powering Modular’s inference services.
• Build fault-tolerant, low toil services able to make use of resources in a variety of hardware platforms Clouds (Tier 1 Cloud Providers & neoclouds)
• Push the envelope for operational excellence with request-to-kernel observability, multi-cloud deployments, cold-start optimizations, and more.
• Build helm charts, kubernetes operators, and more to make a create simple, effective, aintainable deployments.

What you bring to the table:

• 5+ years of experience working in backend engineering
• Experience with Cloud Providers (AWS, GCP, Azure, neoclouds)
• Experience with kubernetes and operating your own services
• A passion for building and operating high performance, low toil, observable systems.
• 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:

• Practical experience implementing & maintaining security in multi-tenancy environments
• Experience working on high scale ML inference infrastructure (traditional AI or genAI)
• Familiarity with golang

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 $167,000.00 - $273,000.00 USD. 
The estimated base salary range for this role to be performed in Canada, regardless of the province, is $164,000.00 - $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.