Senior Manager, AI Deployment

This is a fully remote position, open to applicants in California.

📋 Description

• Drive the strategy, roadmap, and operational plan for AI model performance and inference quality.

• Set performance budgets for latency, throughput, memory, GPU usage, power, and numerical parity.

• Lead investigations into performance bottlenecks across model architecture, operators, kernels, memory movement, scheduling, runtime behavior, and hardware utilization.

• Implement repeatable benchmarking and profiling practices across simulation, hardware-in-the-loop, bench, and vehicle environments.

• Guide optimization efforts through modifications in model architecture, enhancements to operators and kernels, memory optimization, scheduling, and hardware-aware execution.

• Create performance dashboards, regression detection tools, benchmark automation processes, and root-cause diagnostics.

• Collaborate with teams in Embodied AI, model development, GPU kernel, runtime, system performance, vehicle integration, simulation, and safety.

• Convert profiling outcomes into actionable recommendations for model architects and researchers.

• Represent AI Deployment in architecture reviews, program planning, and discussions with senior leadership.

• Build and lead an inclusive, high-performing team through hiring, coaching, feedback, and manager development.

• Define ownership, priorities, staffing plans, and operational rhythms across performance workstreams.

• Establish and manage KPIs for inference latency, latency variability, throughput, memory efficiency, GPU usage, parity, and regression rate.

• Balance immediate production requirements with long-term investments in profiling, optimization automation, reduced precision, and performance infrastructure.

• Address cross-functional challenges and align stakeholders on performance, quality, and implementation trade-offs.

• Cultivate technical leaders and succession plans in GPU performance, model optimization, inference systems, and numerical analysis.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Electrical or Computer Engineering, Robotics, Machine Learning, or a related field; advanced degree preferred, or equivalent experience.

• Over 10 years of experience in machine learning systems, model optimization, inference, GPU systems, robotics, autonomous driving, or a related field.

• More than 5 years of experience in people leadership, including managing managers or senior technical leaders.

• Proven experience in deploying production machine-learning inference systems on GPU, accelerators, robotics, automotive, or other edge hardware.

• Strong understanding of factors influencing model performance including architecture, tensor shapes, operators, kernels, memory movement, scheduling, runtime execution, and hardware utilization.

• Practical experience with several tools such as PyTorch, CUDA, C++, Python, TensorRT, GPU profiling, benchmarking, performance analysis, or inference runtimes.

• Familiarity with techniques like quantization, pruning, distillation, architecture optimization, kernel optimization, or memory optimization.

• Experience in building benchmark automation, performance regression detection, telemetry, dashboards, or profiling workflows.

• Excellent systems thinking, communication, decision-making, and cross-functional leadership capabilities.

• Experience optimizing real-time machine-learning systems for autonomous driving, robotics, embedded systems, or computer vision.

• Knowledge of GPU performance metrics, including memory bandwidth, occupancy, synchronization, stream scheduling, or device-to-device data movement.

• Proficiency with tools such as NVIDIA Nsight Systems, NVIDIA Nsight Compute, PyTorch Profiler, TensorRT profiling tools, or similar.

• Experience in deploying reduced-precision models and managing calibration, sensitivity, parity, and model-quality risks.

• Background in optimizing transformer, vision, lidar, or multimodal workloads.

• Experience in measuring performance across simulation, hardware-in-the-loop, bench, and vehicle environments.

• Familiarity with safety-critical or highly reliable systems.

🏝️ Benefits

• Bonus potential through an incentive pay program based on company performance, job level, and individual performance.

• Medical, dental, and vision benefits.

• Health Savings Account.

• Flexible Spending Accounts.

• Retirement savings plan.

• Sickness and accident benefits.

• Life insurance.

• Paid vacation and holidays.

• Remote work with no expected reporting to a GM worksite unless directed by the manager.

• Travel under 25%.

• Potential eligibility for relocation benefits.