Inference Systems Engineer

Modular is hiring an Inference Systems Engineer. It pays $148k-$270k a year and Level rates it ; you can apply on Level.

AI in this role

Design and build robust, high-performance distributed systems abstractions and infrastructure for state-of-the-art AI model inference.

vllm

distributed-systemsconcurrencyperformance-tuning

About Modular

At Modular, a Qualcomm company, we’re on a mission to revolutionize AI infrastructure by systematically rebuilding the AI software stack from the ground up. Our team, made up of industry leaders and experts, is building cutting-edge, modular infrastructure that simplifies AI development and deployment. By rethinking the complexities of AI systems, we’re empowering everyone to unlock AI’s full potential and tackle some of the world’s most pressing challenges.
If you’re passionate about shaping the future of AI and creating tools that make a real difference in people’s lives, we want you on our team. You can read about our culture and careers to understand how we work and what we value.

About the role:

ML developers today face significant friction in taking trained models into deployment. They work in a highly fragmented space, with incomplete and patchwork solutions that require significant performance tuning and non-generalizable, model-specific enhancements. At Modular, we are building the Modular platform: a next generation AI platform that will radically improve the way developers build and deploy AI models.
State-of-the-art inference is a fast moving target - every few months brings new model architectures, new attention variants, new parallelism strategies, and new optimization techniques (e.g. new variants of disaggregation, speculative decoding, hierarchical KV caching, constrained decoding, MoE routing, and more). Building each feature right in isolation is already a challenge, but designing for clean composability and long tail reliability brings it to another level.
LOCATION: Candidates based in the United States 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.

What you will do:

This team's job is to identify the opportunities for unifying structures underneath today’s inference features, and turn them into the robust and flexible distributed-systems abstractions and building blocks to make tomorrow's features ship faster and more robustly. Responsibilities would include:

Leading cross-team architecture-level decisions on concurrency, data movement, and system boundaries in a fast-moving in-house software stack spanning from the metal to the cloud.
Developing flexible and extensible interfaces for open-source model developers and community contributors that are not just enjoyable to use, but implicitly promote best practices.
Designing highly modular systems with clear contracts to create components with unambiguous ownership, testability, and scaffolding for robust agentic development.

What you bring to the table:

5+ years of systems programming experience with a focus on performance, concurrency, and distributed architectures
Strong instinct for API and abstraction design, and the ability to present a strong case for design proposals
Comfortable working in Python, Rust, Mojo, or similar (in the agentic-coding era, we believe deep understanding of software systems supersedes knowing language syntax)
Intuition and ability to reason about pathological performance bottlenecks in complex systems, and translate that into concrete diagnostics and solutions
Demonstrated ownership of software solutions from implementation through production support. You bring war stories from learning the hard way why defensive engineering is worth the extra investment.
Genuine interest in the research literature and solid instincts for what's worth building

Helpful Experience

Experience under the hood deep inside a high-performance ML inference system (vLLM, SGLang, TRT-LLM, etc.)
Broad systems programming experience: lock-free and wait-free data structures, POSIX APIs and architecture, memory-layout aware optimization, NUMA-aware design, zero-copy transport, etc.
Experience with graph-based (dataflow, actor) programming models and runtimes
Experience with GPU or accelerator programming, especially optimizing around async scheduling and data transfer.

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, regardless of the state, is $148,000.00 - $270,000.00 USD.
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.

How we rate this

Inference Systems Engineer at Modular rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Inference Systems Engineer is an open job at Modular.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $148k-$270k a year.
The role is based in United States - Remote and is marked remote.
The job was posted on August 27, 2026.
Seniority: senior.
Applications are handled by Modular. Apply on Modular

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Distributed SystemsConcurrencyPerformance TuningvLLM

Questions you could be asked

Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where concurrency was part of your work. What did you do?
Tell me about a project where performance tuning was part of your work. What did you do?
What's a project where you used vLLM hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Distributed Systems, Concurrency, Performance Tuning, and vLLM. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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