Senior Deep Learning Compiler Verification Engineer

NVIDIA•Redmond, WA

About The Position

NVIDIA is transforming computer graphics, PC gaming, and accelerated computing, now leveraging AI to define the next era of computing where GPUs act as the brains of computers, robots, and self-driving cars. We are building the next generation of compiler technologies to accelerate deep learning workloads and are seeking an engineer to implement compiler verification software and related infrastructure in the AI space. This role involves solving critical problems alongside diverse minds in GPU computing and systems software.

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++.
Experience with one major DL framework (PyTorch, JAX/XLA, TensorRT, or similar), involving 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.

Nice To Haves

Compiler engineering experience including LLVM, MLIR, TVM, or XLA — understanding 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.

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 (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, exploring 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.