Asks for

The ad says“Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.”
The ad says“Comfort with the deployment surface around serving: containers, Kubernetes, GPU drivers and runtimes, and the usual ways a cluster refuses to cooperate.”
The ad says“Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.”
The ad says“Comfort with the deployment surface around serving: containers, Kubernetes, GPU drivers and runtimes, and the usual ways a cluster refuses to cooperate.”
The ad says“3-5 years of professional engineering experience, with at least some of it spent deploying or operating ML systems in production. You have run inference workloads yourself, not just written about them.”
The ad says“Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.”
The ad says“Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.”
The ad says“Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.”
The ad says“Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang.”
The ad says“Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang.”
The ad says“Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang.”
The ad says“Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang.”

About the role:

We are looking for a Developer Advocate to evangelize the MAX Platform's inference and serving capabilities with our user base and developer community. This involves creating technical content such as user guides and blog posts as well as giving talks at conferences, leading workshops, all with the goal of enabling our community of builders deploying models in production.

Join our world-leading product team and be part of redefining how AI infrastructure is built and deployed.

LOCATION: Candidates based in the US or Canada are welcome to apply. To support growth and collaboration, those in earlier career stages work in a hybrid capacity at our Los Altos, CA. More senior staff can work out of our office in Los Altos, CA or remotely from home. Onboarding for new hires is conducted in-person in our Los Altos, CA office.

Additionally, this role requires travel to conferences and developer events, which may be as often as once per month, as well as travel for team and company events (typically 2-4 times per year).

What you will do:

Build and publish reproducible benchmarks comparing MAX against vLLM, SGLang, TensorRT-LLM, and Triton Inference Server, including methodology, harness code, and hardware configurations so others can verify the numbers.
Deploy and profile real inference workloads on MAX across CPU and GPU targets, and investigate performance gaps in latency, throughput, and cost per token.
Write and maintain technical content grounded in that work: performance deep dives, serving architecture explainers, and posts that show how MAX handles batching, KV cache management, quantization, and multi-GPU serving.
Author runnable tutorials, examples, and video walkthroughs covering model deployment on MAX, from pip install modular to a served endpoint under load.
Provide technical support to engineers evaluating MAX for inference and serving, including teams at some of the world's largest companies, and debug their deployment and performance issues directly.
Answer technical questions across GitHub, Discord, X, and LinkedIn, reproducing reported issues and filing them with enough detail for engineering to act on.
Translate community and customer findings into prioritized inference and serving feedback for engineering and product, shaping the MAX roadmap.
Present technical talks, benchmark results, and live demos at conferences, summits, and meetups.
Help set the technical direction of Modular's developer relations content, including what gets benchmarked, documented, and demoed next.

What you bring to the table:

3-5 years of professional engineering experience, with at least some of it spent deploying or operating ML systems in production. You have run inference workloads yourself, not just written about them.
Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang.
Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code.
Comfort with the deployment surface around serving: containers, Kubernetes, GPU drivers and runtimes, and the usual ways a cluster refuses to cooperate.
You benchmark rigorously. You know why one number is not a result, how to control for warmup and batch size, and when a comparison is not apples to apples.
You write well about technical work, and you have a portfolio to show it: blog posts, tutorials, videos, docs, or courses. You explain complex ideas without losing precision, and you cut the filler.
You learn new tools fast and turn them into accurate content quickly. A feature ships Tuesday, your tutorial goes out Thursday and the code runs.
A growth and leadership mindset, with a collaborative attitude that seeks to learn more from our customers, team members, and the broader market

Minimum Qualifications:

Bachelor's degree in Engineering, Information Systems, Computer Science, or technical related field.
2+ years of Product Management or related work experience.

*Completed advanced degrees in a relevant field may be substituted for up to two years of work experience.

Helpful, but not required:

Experience programming GPUs using CUDA or ROCm.
Familiarity with the Mojo 🔥 programming language and MAX AI framework.
Familiarity with open source software development practices and communities.
Experience producing high-quality videos covering technical topics.

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. Your benefits package may include comprehensive healthcare coverage, retirement and savings programs, employee stock purchase opportunities, paid time off, wellbeing resources, family support programs, and learning and development opportunities. Please note that specific benefit packages may vary based on your location, you can read more about benefits offered by Qualcomm here.
Competitive Compensation. We offer very strong compensation packages, including RSU grants. 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 is $155,400.00 - $233,000.00 USD.

The estimated base salary range for this role to be performed in Canada is $118,200.00 - $177,200.00 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 upcoming openings that are lower/higher level than the ones advertised.