Senior Manager, AI Deployment

General Motors • Sunnyvale, CA

1d • $296,300 - $453,900 • Remote

About The Position

General Motors is developing software and artificial intelligence capabilities for autonomous driving. Within AI Foundations, AI Acceleration improves machine-learning model speed, efficiency, and reliability on production vehicle hardware. The AI Deployment team owns model inference performance across simulation, hardware-in-the-loop, bench, and vehicle environments, focusing on latency, memory, GPU utilization, numerical parity, profiling, benchmarking, reduced precision, and production readiness.

The Senior Manager, AI Deployment leads the strategy and execution of model performance and on-vehicle inference for autonomous driving. The role leads engineering managers and senior technical leaders across model optimization, GPU systems, inference runtimes, and vehicle integration. It sets performance goals, guides optimization of complex autonomy models, and establishes disciplined methods to measure latency, diagnose regressions, and validate improvements. Success requires technical judgment, people leadership, and clear trade-offs among latency, memory, throughput, accuracy, power, and numerical parity.

This role is categorized as remote. The selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager. Travel of less than 25% is required. Relocation benefits are available to candidates who qualify under company policy.

Requirements

• Bachelor’s degree in Computer Science, Electrical or Computer Engineering, Robotics, Machine Learning, or a related field, or equivalent experience; an advanced degree is preferred.
• 10+ years of experience in machine learning systems, model optimization, inference, GPU systems, robotics, autonomous driving, or a related field.
• 5+ years of people-leadership experience, including experience leading managers or senior technical leaders.
• Experience shipping production machine-learning inference systems on GPU, accelerator, robotics, automotive, or other edge hardware.
• Strong understanding of model-performance factors, including architecture, tensor shapes, operators, kernels, memory movement, scheduling, runtime execution, and hardware utilization.
• Hands-on experience with several of PyTorch, CUDA, C++, Python, TensorRT, GPU profiling, benchmarking, performance analysis, or inference runtimes.
• Experience with quantization, pruning, distillation, architecture optimization, kernel optimization, or memory optimization.
• Experience building benchmark automation, performance regression detection, telemetry, dashboards, or profiling workflows.
• Strong systems thinking, communication, decision-making, and cross-functional leadership skills.

Nice To Haves

• Experience optimizing real-time machine-learning systems for autonomous driving, robotics, embedded systems, or computer vision.
• Deep experience with GPU performance, memory bandwidth, occupancy, synchronization, stream scheduling, or device-to-device data movement.
• Experience with NVIDIA Nsight Systems, NVIDIA Nsight Compute, PyTorch Profiler, TensorRT profiling tools, or equivalent tools.
• Experience deploying reduced-precision models and managing calibration, sensitivity, parity, and model-quality risks.
• Experience optimizing transformer, vision, lidar, or multimodal workloads.
• Experience measuring performance across simulation, hardware-in-the-loop, bench, and vehicle environments.
• Experience with safety-critical or highly reliable systems.
• Advanced degree.

Responsibilities

• 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.
• Build performance dashboards, regression detection, benchmark automation, and root-cause diagnostics.
• Partner with Embodied AI, model development, GPU kernel, runtime, system performance, vehicle integration, simulation, and safety teams.
• Translate profiling results into clear recommendations for model architects and researchers.
• Represent AI Deployment in architecture reviews, program planning, and senior leadership discussions.
• Build and lead an inclusive, high-performing organization through hiring, coaching, feedback, and manager development.
• Establish clear ownership, priorities, staffing plans, and operating rhythms across performance workstreams.
• Define and manage KPIs for inference latency, latency variability, throughput, memory efficiency, GPU utilization, parity, and regression rate.
• Balance near-term production needs with longer-term investments in profiling, optimization automation, reduced precision, and performance infrastructure.
• Resolve cross-functional issues and align stakeholders when performance, quality, or implementation trade-offs are contested.
• Develop technical leaders and succession plans in GPU performance, model optimization, inference systems, and numerical analysis.

Benefits

• Medical, dental, and vision benefit options.
• Health Savings Account and Flexible Spending Accounts.
• Retirement savings plan.
• Sickness and accident benefits.
• Life insurance.
• Paid vacation and holidays.
• Incentive pay program with payouts based on company performance, job level, and individual performance.
• Relocation benefits for candidates who qualify under company policy.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior