General Motors (GM)

Senior Manager, AI Deployment

Remote, US$296,300-$453,900Posted 1 day ago

Job Description

Job Description

About the Organization

General Motors is developing the software and artificial intelligence capabilities for the next generation of autonomous driving. Within AI Foundations, AI Acceleration makes machine learning models faster, more efficient, and more reliable on production vehicle hardware.

The AI Deployment team owns model inference performance across simulation, hardware-in-the-loop, bench, and vehicle environments. The team focuses on latency, memory, GPU utilization, numerical parity, profiling, benchmarking, reduced precision, and production readiness.

About the Role

We are looking for a Senior Manager, AI Deployment to lead the strategy and execution of model performance and on-vehicle inference for autonomous driving. You will lead engineering managers and senior technical leaders working across model optimization, GPU systems, inference runtimes, and vehicle integration. You will set performance goals, guide optimization of complex autonomy models, and establish disciplined methods to measure latency, diagnose regressions, and validate improvements. Success requires strong technical judgment, people leadership, and the ability to make clear trade-offs among latency, memory, throughput, accuracy, power, and numerical parity.

What You’ll Do

  • Own the strategy, roadmap, and operating plan for AI model performance and inference quality.
  • Establish performance budgets for latency, throughput, memory, GPU utilization, power, and numerical parity.
  • Lead investigations into performance bottlenecks across model architecture, operators, kernels, memory movement, scheduling, runtime behavior, and hardware utilization.
  • Establish repeatable benchmarking and profiling practices across simulation, hardware-in-the-loop, bench, and vehicle environments.
  • Guide optimization through model architecture changes, operator and kernel improvements, memory optimization, scheduling, and hardware-aware execution.
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