Engineering Manager, AI Compiler Analysis

Responsibilities

• 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.

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