About The Job
The compiler is central to FuriosaAI's mission to build high-performance, energy-efficient AI systems. The front end is where the compiler meets the outside world. Its mission spans three areas:
- Faithful Ingestion: Translate models from external frameworks — with their evolving semantics, dynamic behaviors, and framework-specific constructs — into a precise internal representation that the rest of the compiler can reason about with confidence.
- Structural Optimization: Reshape programs at the graph level — through operator fusion, constant propagation, and shape resolution — so that downstream compilation stages receive the cleanest possible input.
- Tensor-Level Kernel Language Design: Design and evolve a programming language that enables users to directly author models optimized for FuriosaAI hardware. As the user-level interface to the compiler's internal IR and DSL, this language should maximize hardware performance while remaining intuitive for a broad range of users.
We are looking for someone who thinks in systems, designs for extensibility, and brings rigor and clarity across the stack — from model ingestion to user-facing language design.
Responsibilities
- Design and implement the front-end pipeline that transforms models from major deep learning frameworks such as PyTorch into the compiler's internal IR.
- Develop graph-level optimizations, including operator fusion, constant folding, shape inference, and layout transformations.
- Build extensible model ingestion structures that can accommodate new architectures such as LLM, VLA, and Multimodal models, and custom operators, while maintaining consistency and correctness.
- Design and evolve a tensor-level kernel language that exposes the capabilities of the internal IR and DSL through a consistent, well-abstracted user interface.
- Establish verification mechanisms to ensure correctness throughout the translation process.
- Collaborate with software teams and language users to maximize end-to-end compilation quality and refine the language design based on real-world usage patterns.
Minimum Qualifications
- Bachelor's degree in Computer Science, Mathematics, or a related field.
- Experience or familiarity with compilers, program transformation systems, or related infrastructure.
- Understanding of deep learning frameworks such as PyTorch, TensorFlow, and ONNX — and their model representations.
- Ability to abstract complex system constraints into consistent, user-friendly programming interfaces.
- Proficiency in Python and experience with at least one systems programming language such as Rust or C++.
Preferred Qualifications
- Master's or PhD in Programming Languages, Compilers, Program Analysis, or related fields.
- Experience designing and implementing domain-specific languages (DSLs) or user-facing programming models.
- Deep understanding of PyTorch compiler internals (TorchDynamo, FX Graph, torch.compile, torch.export) or kernel programming languages such as Triton.
- Research or industry experience with compiler frameworks such as LLVM, MLIR, or TVM.
- Understanding of AI accelerator architectures (NPU, GPU, TPU) and their implications for programming model design.
- Experience with graph-level compilation optimizations or contributions to open-source compiler and deep learning framework projects.
Contact
