Engineering Manager, AI Compiler Analysis

NVIDIA; Austin (TX); On-site; USD 168,000 - 322,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an Engineering Manager to lead verification of next-generation AI compilers and workloads. This hands-on leadership role focuses on ensuring high-quality AI compiler transformations and generated GPU programs through formal and empirical verification methods.

You will collaborate with AI compiler development, CUDA software, ML frameworks, and runtime teams to scale verification infrastructure and accelerate engineering velocity. Equity and comprehensive benefits are provided.

Qualifications

• BS, MS, or PhD in CS/CE or equivalent experience.
• 10+ years of relevant software engineering experience, including 3+ years leading teams.
• Experience with AI compiler or framework technologies such as MLIR, TensorRT, XLA, Triton, PyTorch, or JAX.
• Fluency in AI workload and ML framework concepts: graphs, tensor operations, model execution, and training/inference workflows.
• Strong people management including hiring, mentoring, performance management, and team development.

Responsibilities

• Lead, mentor, and grow a highly technical team responsible for AI compiler verification.
• Own verification of next-gen AI workloads across the AI compiler stack and execution pipeline.
• Define formal verification requirements for AI compiler transforms and generated GPU programs.
• Drive AI-assisted and compiler-aware verification techniques (adversarial workloads, differential testing, symbolic reasoning, formal methods, fuzzing).
• Partner with AI compiler development, CUDA software, ML framework, runtime, product, and AI software teams to build scalable verification infrastructure.

Skills

AI compiler verification

People management

MLIR/TensorRT/XLA/Triton/PyTorch/JAX

AI workload concepts

Education

BS/MS/PhD in CS/CE

Tools

MLIR

TensorRT

XLA

Triton

PyTorch

JAX

Job description

NVIDIA's invention of the GPU transformed computer graphics, parallel computing, and modern AI. Today, NVIDIA high-performance processing platforms power breakthroughs across generative AI, autonomous systems, scientific computing, robotics, and high-performance data centers.

NVIDIA's compiler technologies are key enablers of AI at scale, turning rapidly evolving deep learning models into highly optimized GPU programs for training and inference. As AI models, GPU architectures, and compiler systems become more sophisticated, AI compiler quality has become a deep technical challenge at the intersection of compilers, machine learning frameworks, numerical computing, formal reasoning, and large-scale systems engineering. To address these complex challenges, we are seeking an Engineering Manager to spearhead our strategy for verifying AI compilers built for next-generation deep learning workloads. This is a hands-on compiler engineering leadership role for someone who understands where compiler quality can regress in modern AI compiler stacks.

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most brilliant and hardworking people in the world working with us and our product lines are growing fast in some of the hottest state of the art fields such as Virtual Reality, Artificial Intelligence, Deep Learning and Autonomous Vehicles.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD for Level 2, and 200,000 USD - 322,000 USD for Level 3.

You will also be eligible for equity and benefits.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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What You'll Be Doing

• Lead, mentor, and grow a highly technical team responsible for AI compiler verification.
• Own the verification of next-generation AI workloads, including LLMs and agentic AI systems, across the full spectrum of the AI compiler stack and execution pipeline.
• Define formal-verification requirements for AI compiler transformations and generated GPU programs, including formal specifications, tensor/operator semantics, semantic preservation, code equivalence, numerical behavior, and properties stressed by AI-generated or adversarial workloads.
• Drive the use of AI-assisted and compiler-aware verification techniques, including adversarial workload generation, differential testing, symbolic reasoning, formal methods, fuzzing, static analysis, and automated debugging.
• Partner closely with AI compiler development, CUDA software, ML framework, runtime, product, and AI software teams to build scalable verification infrastructure, improve engineering velocity, and increase production confidence.

What We Need To See

• BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.
• 10+ overall years of total relevant software engineering experience, including at least 3 years experience leading engineering teams or major technical initiatives.
• Experience with AI compiler or framework technologies such as MLIR, TensorRT, XLA, Triton, PyTorch, or JAX.
• Fluency with AI workload and ML framework concepts, including computation graphs, tensor operations, model execution, and training or inference workflows.
• Strong people management skills, including hiring, mentoring, performance management, and team development.

Ways To Stand Out From The Crowd

• Hands-on with deep learning compiler internals, including compiler IRs, optimization and lowering pipelines, code generation, runtime integration, or production compiler infrastructure.
• Experience verifying performance-sensitive compiler behavior and root-causing subtle regressions in production AI/ML systems using computational methods, fuzzing, code inspection, or automated debugging.
• Background in formal verification or programming languages, with familiarity in formal specifications, theorem proving, Lean, SMT/SAT solvers, or symbolic reasoning.