Senior Manager, AI Deployment
General Motors
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Benefits offered by this job
Job summary
General Motors is seeking 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 across model optimization, GPUs, and runtimes, balancing latency, memory, throughput, and power.
The role is categorized as remote in the United States, with travel up to 25% and relocation benefits for eligible candidates.
Qualifications
- Bachelor's degree in CS, EE, Robotics, ML or related field; advanced degree preferred, or equivalent experience.
- 10+ years of experience in machine learning systems, model optimization, inference, GPU systems, robotics, or autonomous driving.
- 5+ years of people leadership, including leading managers or senior technical leaders.
- Experience shipping production ML inference systems on GPU, accelerator, robotics, automotive, or edge hardware.
- Strong understanding of architecture, tensor shapes, operators, kernels, memory movement, scheduling,_runtime execution, and hardware utilization.
- Hands-on with 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, telemetry, dashboards, or profiling workflows.
- Strong systems thinking, communication, decision-making, and cross-functional leadership.
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.
- 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.
- 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.
Skills
Education
Tools
Job description
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.
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.
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.
- 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.
- 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.
- Bachelor’s 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.
- 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.
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: $296,300 - $453,900 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
This role is categorized as remote. This means 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. The selected candidate will be required to travel <25% for this role. Relocation benefits are available for candidates who qualify under company policy.
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment.
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
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