Engineering Manager, AI Compiler Analysis

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +2

Engineering Manager to lead a team focused on verifying AI compilers for next-generation deep learning workloads, including LLMs and agentic AI systems. The role involves defining formal verification requirements, driving AI-assisted verification techniques, and partnering with various AI and software teams.

What you'd actually do

  1. Lead, mentor, and grow a highly technical team responsible for AI compiler verification.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

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, 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.

Nice to have

  • 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.

What the JD emphasized

  • AI compiler quality
  • formal verification
  • compiler quality

Other signals

  • AI compiler verification
  • LLMs and agentic AI systems
  • formal verification
  • compiler quality