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.
Inference Optimization Manager
Location
United States + 1 moreAll locations: United States | Canada
Posted
3 days ago
Salary
$234K - $286K / year
Seniority
Lead
No structured requirement data.
Job Description
Inference Optimization Manager
Modular
Role Description At Modular, we optimize inference from kernel to cloud on one unified stack. We are building a differentiated cloud platform that delivers state of the art inference performance from day one, then keeps getting better. As we learn the shape and patterns of each customer's workload, the platform adapts and improves performance automatically over time. The Performance Labs team builds the infrastructure that makes this possible at scale. We continuously apply the latest optimizations across kernels, the inference engine, and distributed systems so that customer workloads stay on the Pareto frontier of cost and performance. We get there through deep workload insights, a scalable platform, and close collaboration with engineering and product teams. In this role you will lead a high impact team that partners closely with GTM, Product, and Engineering to redefine what an inference platform can be. You will turn real customer workloads into a continuous optimization loop, shape the product direction of Modular Cloud, and build the systems that let performance scale with demand. 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: - Lead the team that builds the optimization platform driving LLM inference on Modular Cloud to state of the art performance across the latest GPU and ASIC architectures. - Own the technical direction of Modular Cloud, delivering LLM performance on the Pareto frontier for agentic use cases and keeping it there as the landscape evolves. - Partner closely with the GTM team to deliver highly customized LLM inference tuned to specific customer workloads, and collaborate across engineering to drive optimizations spanning the full stack, from GPU kernels to cloud infrastructure. - Translate insights from customer engagements into a clear technical roadmap that engineering teams can execute against. - Grow and develop a high impact team, set priorities, and create the conditions for engineers to do their best work on hard performance problems. - Champion the team externally through blog posts on innovative approaches to LLM inference optimization that shape industry wide best practices. Qualifications - 5+ years in distributed systems or performance engineering, including experience leading or managing engineering teams. - A track record of shipping durable, reusable software tools and libraries adopted across teams and functions, and of guiding a team to do the same. - Sound judgment in evaluating technical tradeoffs and setting priorities, paired with strong communication and technical leadership skills. - The ability to translate ambiguous customer and product needs into focused engineering direction. - Creativity and curiosity in solving complex problems, a collaborative and team oriented mindset, and alignment with our culture. Requirements - Hands on background in GPU kernel programming, inference engine internals, or distributed inference architectures. - Experience with Kubernetes and cloud native ecosystems. - Familiarity with modern LLM architectures and the latest inference optimization techniques. Benefits - 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! - 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 $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 $229,000.00 - $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.
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