Deep Learning Compiler Verification Engineer

About the role

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.

We are building the next generation of compiler technologies to accelerate deep learning workloads. We are looking for an engineer to implement compiler verification software & related infrastructure in the AI space. You will be solving critical problems working alongside a diverse set of minds in GPU computing and systems software, doing what you enjoy. If this sounds like a fun challenge, we want to hear from you.

What you'll be doing:

- Develop and maintain components of systems to reason about correctness in deep learning compilers, across graph transformations, IR lowering, and GPU execution.

- Work with deep learning compiler and architecture teams to analyze and validate optimizations (e.g., graph rewrites in MLIR, fusion passes, mixed-precision transformations), ensuring they preserve semantics and numerical behavior.

- Implement and extend test generation systems that use deep learning solutions and analysis methods to drive in-depth testing. These systems explore the vast combinatorial space of model topologies, precision modes, and hardware targets.

- Contribute to improving how we measure functional quality and performance as models, compiler stacks, and hardware continue to evolve.

What we need to see:

- BS, MS or PhD in Computer Science, Computer Engineering, Mathematics, or equivalent experience.

- 2+ years of hands-on software engineering experience, with experience in compiler development, deep learning systems, or compiler verification.

- Proficiency in Python or C++ and experience with one major DL framework. This could be PyTorch, JAX/XLA, TensorRT, or a similar framework. Experience should involve model execution, graph representation, or runtime behavior.

- Strong problem-solving and debugging skills — ability to reason about model behavior and compiler-generated code, investigate correctness issues, and collaborate with teammates to resolve edge-case failures.

Ways to stand out from the crowd:

- Experience with compiler technologies such as LLVM, MLIR, TVM, or XLA, and familiarity with compiler passes, IR semantics, or correctness testing.

- Exposure to formal methods or language specification: type systems, program semantics, or proof-based verification.

- Experience with DL model internals such as quantization, operator fusion, mixed-precision, or graph-level optimization.