Deep Learning Compiler Verification Engineer

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CAAustin, TXHillsboro, ORSeattle, WA
Salary: $116,000–$189,750 / yr
Employment: Full-time
Posted: 6 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $203k

This role $153k

$101k $257k

below market most similar roles pay here above market

This role pays less than 91% of similar roles. Most pay $177,200–$229,100 — the blue band above. At the midpoint, this role pays about $153k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1805 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1196 roles with salary data.

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At a glance

TL;DR · Deep Learning Compiler Verification Engineer

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The Deep Learning Compiler Verification Engineer joins the team building next-generation compiler technologies to accelerate deep learning workloads. In this role, you will develop and maintain systems to reason about correctness in deep learning compilers, specifically focusing on graph transformations, IR lowering, and GPU execution. You will analyze and validate optimizations like graph rewrites in MLIR, fusion passes, and mixed-precision transformations to ensure they preserve numerical behavior. Key responsibilities include implementing test generation systems using deep learning solutions to explore combinatorial spaces of model topologies and hardware targets. Required skills include proficiency in Python or C++, experience with major DL frameworks like PyTorch or JAX/XLA, and knowledge of compiler technologies such as LLVM or TVM. You will solve critical problems regarding model execution and runtime behavior.

Skills

Python C++ MLIR LLVM PyTorch JAX XLA TensorRT TVM Deep Learning Compiler Verification IR Lowering Graph Transformations Mixed-Precision Quantization Operator Fusion Formal Methods Systems Software

What you'll do

Develop and maintain systems to reason about correctness in deep learning compilers.
Analyze and validate optimizations across graph transformations, IR lowering, and GPU execution.
Ensure that graph rewrites, fusion passes, and mixed-precision transformations preserve numerical behavior.
Implement and extend test generation systems using deep learning solutions and analysis methods.
Explore combinatorial spaces of model topologies, precision modes, and hardware targets for testing.
Improve metrics for measuring functional quality and performance across compiler stacks and hardware.
Investigate and resolve correctness issues and edge-case failures in compiler-generated code.

What we're looking for

BS, MS, or PhD in Computer Science, Computer Engineering, Mathematics, or equivalent experience.
2+ years of hands-on software engineering experience in compiler development, deep learning systems, or compiler verification.
Proficiency in Python or C++.
Experience with one major DL framework such as PyTorch, JAX/XLA, or TensorRT.
Experience involving model execution, graph representation, or runtime behavior.
Strong problem-solving and debugging skills to reason about model behavior and compiler-generated code.
Experience with compiler technologies such as LLVM, MLIR, TVM, or XLA (preferred).
Exposure to formal methods, language specification, or DL model internals such as quantization and operator fusion (preferred).

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