AI Library Engineer

Modular•United States,

8h•$167,000 - $185,000•Remote

About The Position

At Modular, we are building a next generation AI platform to power modern applications and facilitate access to cutting-edge hardware. The MAX Framework is our developer-facing layer: it defines the APIs developers use to express models, integrate custom kernels, orchestrate execution, iterate on quality and ship systems into production. As an AI Library Engineer for MAX, you will work with core APIs and developer experience for inference and training of AI models. You will work at the intersection of API design, systems engineering, and modern AI frameworks. Your output will be the implementations that make MAX feel coherent, powerful, and intuitive to use — while preserving performance, portability, maintainability and TCO.

Requirements

Passion for designing developer-facing APIs (SDKs, frameworks, or platforms).
Good understanding of modern AI frameworks and their design tradeoffs (e.g., PyTorch, JAX, TensorFlow, vLLM, XLA/MLIR-adjacent ecosystems).
Proficiency in one or more systems / performance languages (C++, Rust, Go) and one or more user-facing languages (Python; familiarity with Mojo is a plus).
Good instincts and a passion for API ergonomics: naming, composability, types, error handling, configurability, and clarity.
Strong written communication: you can write specs that engineers can implement without ambiguity.
Passion for engineering standards, pragmatism, and a bias towards incremental development without compromising long term design.

Nice To Haves

Prior experience in an AI framework or library.
Prior experience in developer experience research.
Prior experience designing programming languages and abstractions

Responsibilities

Develop the API surface for MAX: namespaces, core abstractions, extension points, programming model, compatibility guarantees and developer experience.
Develop and design inference APIs that support real-world serving needs: model loading, distributed inference, quantization, tokenization/pipelines, configuration surfaces, batching/streaming, and deployment-oriented ergonomics.
Develop training APIs that scale from single device to distributed execution, with clear primitives for device placement, parallelism, checkpointing, and observability.
Contribute to a coherent programming model across Python and Mojo-adjacent surfaces: align naming, types, and conventions; avoid leaky abstractions; define the "pit of success".
Work on technical RFCs and technical specs: write and socialize proposals; gather feedback from internal model engineers and external users; iterate towards consensus.
Partner cross-functionally with compiler/runtime, kernels, cloud/serving, and documentation/DevRel teams to ensure APIs map cleanly to underlying capabilities.
Develop reference implementations and exemplar code: golden-path examples, architecture templates, and best-practice patterns that teams can copy.