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will be dynamic and deeply technical, placing you at the center of compiler innovation. You will lead the design, implementation, optimization, and maintenance of the core compiler technologies that accelerate massive deep learning workloads. This is a highly collaborative role where you ll work alongside the very engineers who built PyTorch for NVIDIA hardware, helping to pioneer new features and stay at the forefront of framework development.

You ll dive deep into performance analysis, scrutinizing workloads running on thousands of GPUs to find optimization opportunities that will shape the future design of Thunder. Furthermore, you will be part of a vibrant ecosystem, working closely with leading compiler, library, and systems teams—including experts behind nvFuser, TVM, XLA, and CUDA—to translate the latest research into practical, high-impact solutions for the open-source community. What we need to see: We are looking for engineers who are excited about building powerful, user-centric tools and are comfortable working in a fast-paced, collaborative environment.

Here are some of the expertise we would like to see: - A Bachelor s, Master s, or Ph.D. in Computer Science or a related technical field (or equivalent experience). - 8+ years of relevant work experience - A strong command of Python and experience building complex, well-tested software systems. - Hands-on experience with deep learning frameworks like PyTorch or JAX. You understand how models are built and where the performance challenges lie. - A solid foundation in compiler concepts such as abstract syntax trees (ASTs), intermediate representations (e.g., SSA form), program analysis, and code generation. - Excellent communication and collaboration skills, essential for working effectively in a distributed, open-source environment. Ways to stand out from the crowd: - Previous contributions to deep learning compiler projects (e.g., TVM, MLIR, IREE) or deep learning frameworks themselves. - Deep expertise in the internals of PyTorch, particularly its compiler stack (TorchDynamo, TorchInductor). - Experience with JAX-like functional transformations and their application in a compiler context. - Familiarity with parallel programming, distributed systems, and writing high-performance CUDA code. - A track record of impactful participation in open-source communities, such as through code contributions, design discussions, or mentorship.

NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility.

As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all. Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: Germany: 112,200 EUR – 194,480 EUR.

Netherlands: 101,250 EUR – 175,500 EUR. Poland: 292,500 PLN – 507,000 PLN.

Zur Position

Join us at the forefront of AI compiler technology and help shape the future of accelerated computing. NVIDIA is seeking passionate engineers to build the next generation of tools used by AI developers and researchers worldwide. Our team is developing Thunder, an ambitious, source-to-source compiler built to unlock outstanding performance for PyTorch models on NVIDIA GPUs.

This is a unique opportunity to contribute to a project that enhances the PyTorch ecosystem, working with modern compiler stacks like PyTorch 2.0 s TorchDynamo and TorchInductor to create powerful, open-source solutions that benefit the entire community. If you are driven to solve complex problems and want to make a foundational impact on the AI ecosystem, apply to join our collaborative and innovative team. What you ll be doing: As a key member of our team, you will be contributing directly to the future of accelerated AI.