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.
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.
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.Skills Required
- BS, MS, or PhD in Computer Science, Computer Engineering, or related field, or equivalent experience.
- 10+ years relevant software engineering experience, including at least 3 years 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 (computation graphs, tensor operations, model execution, training/inference workflows).
- Strong people management skills including hiring, mentoring, performance management, and team development.
- Hands-on experience with deep learning compiler internals (IRs, optimization/lowering pipelines, code generation, runtime integration, production compiler infrastructure).
- Experience verifying performance-sensitive compiler behavior and root-causing subtle regressions using fuzzing, automated debugging, or computational methods.
- Background in formal verification or programming languages; familiarity with theorem proving, Lean, SMT/SAT solvers, or symbolic reasoning.
- Experience with AI-assisted and compiler-aware verification techniques (adversarial workload generation, differential testing, symbolic reasoning, formal methods, static analysis).
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
