NVIDIA

NVIDIA

Posted via Workday

Engineering Manager, AI Compiler Analysis

Posted Aug 5, 2026

Role at a glance

Salary
$168K – $322K/yr
Location
3 Locations, California, United States
Work arrangement
On-site
Employment
Full-time
Experience
10+ overall years of total relevant software engineering experience, including at least 3 years experience leading engineering teams or...
Education
BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.

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Role Summary

AI-generated

The Engineering Manager will lead a technical team responsible for verifying AI compilers that optimize GPU programs for next-generation deep learning workloads, including LLMs and agentic AI systems. The role spans formal verification, compiler-aware testing, numerical behavior, and scalable infrastructure in collaboration with compiler, CUDA, machine learning framework, runtime, product, and AI software teams.

What You'll Do

  • Lead, mentor, and grow a highly technical team responsible for AI compiler verification.
  • Own the verification of next-generation AI workloads across the full spectrum of the AI compiler stack and execution pipeline.
  • Define formal-verification requirements for AI compiler transformations and generated GPU programs.
  • Drive AI-assisted and compiler-aware verification techniques, including adversarial workload generation, differential testing, symbolic...
  • Partner with AI compiler development, CUDA software, ML framework, runtime, product, and AI software teams to build scalable...

Generated from the employer's posting. Verify important details before applying.

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Qualifications

BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience; 10+ years of relevant software engineering experience, including at least 3 years leading engineering teams or major technical initiatives; experience with AI compiler or framework technologies; fluency with AI workload and ML framework concepts; strong people management skills.

Required

  • 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
  • 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...
  • Strong people management skills, including hiring, mentoring, performance management, and team development

Preferred

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

Original job description

Content provided by the employer

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.

Applications for this job will be accepted at least until August 21, 2026.

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.

NVIDIA

About the company

NVIDIA

Large Enterprise

NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.