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. Come join the team and see how you can make a lasting impact on the world.
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:
Design and build 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 sophisticated optimizations (e.g., graph rewrites in MLIR, fusion passes, mixed-precision transformations), ensuring they preserve semantics and numerical behavior
Engineer 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.
Define and improve how we measure and guarantee 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
3+ years of hands-on engineering experience in compiler development, deep learning systems, or compiler verification
Must have deep 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 systems intuition and debugging depth — ability to reason across abstraction layers, from high-level model semantics down to generated code, and track down failures that only manifest in edge cases!
Ways to stand out from the crowd:
Compiler engineering experience including LLVM, MLIR, TVM, or XLA — you understand how passes are composed, how IR semantics are preserved, and where correctness breaks down
Formal methods or language specification background: experience with type systems, program semantics, or proof-based verification
DL model internals depth: experience with quantization, operator fusion, mixed-precision, or graph-level optimization
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 140,000 USD - 224,250 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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