> Markdown version of [/jobs/ext/4517487-deep-learning-compiler-verification-engineer](https://www.wearedevelopers.com/jobs/ext/4517487-deep-learning-compiler-verification-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Deep Learning Compiler Verification Engineer

- **Company:** Nvidia International, Inc.
- **Location:** Seattle, United States
- **Experience:** Experienced
- **Salary:** $116,000.0 - $189,750.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Computer Engineering, Software Debugging, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Software Engineering, Type Systems, Pytorch, Deep Learning, Discretization, Information Technology, Formal Methods, TensorRT, MLIR (Multi-Level Intermediate Representation)
- **Published:** November 1, 2026
- **Apply:** https://startup.jobs/deep-learning-compiler-verification-engineer-2100-nvidia-usa-10646896

## About the Role

* 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.
 * 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 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.

Ways to stand out from the crowd:

 * 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.

## Description

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:

 * 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.