Senior Deep Learning Compiler Verification Engineer

Nvidia

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CAAustin, TXRedmond, WA
Salary: $140,000–$224,250 / yr
Posted: 46 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $210k

This role $182k

$127k most similar roles pay here $262k

This role pays less than 76% of similar roles. Most pay $183,675–$235,750 — the shaded band above. At the midpoint, this role pays about $182k versus about $210k 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 967 open roles on FindRole.

Listed pay typically runs $168,000–$270,250 across 950 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Compiler Verification Engineer

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Join our dynamic team as a senior compiler engineer to develop cutting-edge verification software for deep learning compilers. You will design and build systems that ensure correctness across various transformations and optimizations in GPU execution, collaborating closely with experts in deep learning compiler and architecture teams. Your responsibilities include creating test generation systems using advanced analysis methods and defining metrics to measure functional quality and performance as technologies evolve. Ideal candidates have a strong background in computer science or related fields, at least 3 years of experience in compiler development, and proficiency in Python or C++. Expertise in deep learning frameworks like PyTorch, JAX/XLA, and knowledge of formal methods are essential for this role that tackles complex challenges in the AI domain.

Skills

Python C++ PyTorch JAX TensorRT LLVM MLIR TVM XLA Type Systems Program Semantics Proof-Based Verification Quantization Operator Fusion Mixed-Precision Graph-Level Optimization

What you'll do

Design and build systems to ensure correctness in deep learning compilers.
Analyze and validate sophisticated optimizations in deep learning compilers.
Engineer test generation systems using deep learning solutions for comprehensive testing.
Define and improve metrics for measuring functional quality and performance.
Work with compiler and architecture teams to preserve semantics and numerical behavior.

What we're looking for

BS, MS, or PhD in Computer Science, Engineering, Mathematics, or equivalent experience.
3+ years of hands-on engineering in compiler development or deep learning systems.
Deep proficiency in Python or C++ and experience with major DL frameworks like PyTorch or TensorFlow.
Strong ability to reason across abstraction layers from high-level model semantics to generated code.
Experience with LLVM, MLIR, TVM, XLA, including understanding of IR semantics preservation.
Background in formal methods, type systems, program semantics, or proof-based verification.
In-depth knowledge of DL model internals such as quantization and graph-level optimization.

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