Inference Systems Engineer · Remote

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.

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.

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.

• 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

• Experience under the hood deep inside a high-performance ML inference system (vLLM, SGLang,…