Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 7
Mathematics @ 4
Debugging @ 7
PyTorch @ 7
GPU @ 4
Deep Learning @ 4
AI @ 4
TensorRT @ 7
LLVM @ 3
JAX @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. We are building the next generation of compiler technologies to accelerate deep learning workloads and are looking for an engineer to implement compiler verification software and related infrastructure in the AI space.
Responsibilities
- 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 (for example, 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 across model topologies, precision modes, and hardware targets.
- Define and improve how functional quality and performance are measured and guaranteed as models, compiler stacks, and hardware evolve.
Requirements
- 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.
- Deep proficiency in Python or C++ and experience with at least one major deep learning framework (examples mentioned: PyTorch, JAX/XLA, TensorRT). Experience should involve model execution, graph representation, or runtime behavior.
- Strong systems intuition and debugging skills — ability to reason across abstraction layers from high-level model semantics down to generated code and track down failures that manifest in edge cases.
Preferred / Ways to stand out
- Compiler engineering experience with LLVM, MLIR, TVM, or XLA — familiarity with how passes are composed and how IR semantics are preserved.
- Background in formal methods or language specification (type systems, program semantics, proof-based verification).
- Deep knowledge of DL model internals such as quantization, operator fusion, mixed-precision, or graph-level optimization.
Benefits and Compensation
- The posting states NVIDIA offers highly competitive salaries, equity eligibility, and a comprehensive benefits package. See www.nvidiabenefits.com for company benefits information.
- Base salary range (location- and experience-dependent): 140,000 USD - 224,250 USD.
Additional information
- Applications accepted at least until May 3, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.