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.
ResponsibilitiesDesign 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.
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++.
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.
Skills Required
- Bachelor's degree in Computer Science, Mathematics, or related field
- Experience or familiarity with compilers or program transformation systems
- Understanding of deep learning frameworks like PyTorch and TensorFlow
- Proficiency in Python and experience with Rust or C++
What We Do
FuriosaAI designs and develops data center accelerators for the most advanced AI models and applications. Our mission is to make AI computing sustainable so everyone on Earth has access to powerful AI. Our Background Three misfit engineers with each from HW, SW and algorithm fields who had previously worked for AMD, Qualcomm and Samsung got together and founded FuriosaAI in 2017 to build the world’s best AI chips. The company has raised more than $100 million, with investments from DSC Investment, Korea Development Bank, and Naver, the largest internet provider in Korea. We have partnered on our first two products with a wide range of industry leaders including TSMC, ASUS, SK Hynix, GUC, and Samsung. FuriosaAI now has over 140 employees across Seoul, Silicon Valley, and Europe. Our Approach We are building full stack solutions to offer the most optimal combination of programmability, efficiency, and ease of use. We achieve this through a “first principles” approach to engineering: We start with the core problem, which is how to accelerate.
