Senior Manager, AI Deployment

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 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 Youll 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.
• 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.
• Influence model design by translating profiling results into clear recommendations for model architects and researchers.
• Represent AI Deployment in architecture reviews program planning and senior leadership discussions.

Leadership Responsibilities

• 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.

Your Skills & Abilities (Required Qualifications)

• Bachelors degree in Computer Science Electrical or Computer Engineering Robotics Machine Learning or a related field; advanced degree preferred or equivalent experience.
• 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 the factors that determine model performance: architecture tensor shapes operators kernels memory movement scheduling runtime execution and hardware utilization.
• Hands-on experience with several of the following: 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.

What Will Give You a Competitive Edge

• 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.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York Colorado California or Washington

• Compensation: The expected base compensation for this role is: $296300 - $453900 Actual base compensation within the identified range will vary based on factors relevant to the position.
• Bonus Potential: An incentive pay program offers payouts based on company performance job level and individual performance.
• Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical dental vision Health Savings Account Flexible Spending Accounts retirement savings plan sickness and accident benefits life insurance paid vacation & holidays

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