Deep Learning Compiler Verification Engineer
NVIDIA
This job at a glance
- Who can apply
- 2 years+ of experience
- Must know
- Deep learningPythonC++PyTorch
- Work
- On-site in Austin
- Pay
- Not stated by the employer.
- Skill demand
- Python57%C++28%PyTorch16%Deep learning13%of 100 similar entry-level jobs posted in the US this week
- Listed
- Oct 31, 2026 · still open
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About the Role
NVIDIA is building the next generation of compiler technologies to accelerate deep learning workloads. We are looking for an engineer to implement compiler verification software and 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.
Key Responsibilities
- 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.
Requirements & Tech Stack
- Proficiency in Python or C++.
- Experience with one major DL framework (e.g., PyTorch, JAX/XLA, TensorRT).
- 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.
Qualifications
- 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.
Ways To Stand Out
- 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.