Deep Learning Compiler Verification Engineer

NVIDIA•Austin, TX

5d•$116,000 - $189,750

About The Position

NVIDIA is seeking an engineer to implement compiler verification software and related infrastructure for deep learning systems. The role focuses on developing and maintaining systems that reason about correctness across deep learning compiler graph transformations, intermediate-representation (IR) lowering, and GPU execution. The engineer will work with deep learning compiler and architecture teams to analyze and validate optimizations, including MLIR graph rewrites, fusion passes, and mixed-precision transformations, ensuring they preserve semantics and numerical behavior. The role also involves building test-generation systems that use deep learning solutions and analysis methods to explore combinations of model topologies, precision modes, and hardware targets, and improving measures of functional quality and performance as models, compiler stacks, and hardware evolve. The expected base salary range is USD 116,000–189,750. The position is eligible for equity and benefits.

Requirements

Bachelor’s, master’s, or PhD in Computer Science, Computer Engineering, Mathematics, or equivalent experience.
At least 2 years of hands-on software engineering experience, including experience in compiler development, deep learning systems, or compiler verification.
Proficiency in Python or C++.
Experience with a major deep learning framework such as PyTorch, JAX/XLA, TensorRT, or a similar framework, involving model execution, graph representation, or runtime behavior.
Strong problem-solving and debugging skills, including the ability to reason about model behavior and compiler-generated code, investigate correctness issues, and collaborate to resolve edge-case failures.

Nice To Haves

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, including type systems, program semantics, or proof-based verification.
Experience with deep learning model internals such as quantization, operator fusion, mixed-precision, or graph-level optimization.

Responsibilities

Develop and maintain systems for reasoning 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, ensuring they preserve semantics and numerical behavior.
Implement and extend test-generation systems that use deep learning solutions and analysis methods to test combinations of model topologies, precision modes, and hardware targets.
Contribute to improving measurements of functional quality and performance as models, compiler stacks, and hardware evolve.