This role pays more than
99%
of similar roles. Most pay
$177,568–$246,150
— the shaded band above.
At the midpoint, this role pays about
$375k
versus about
$212k
for comparable roles.
Based on 240 similar postings.
Employer
About General Motors (GM)
General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.
General Motors (GM) currently has
123 open roles
on FindRole.
Listed pay typically runs
$160,200–$245,500
across 64 roles with salary data.
As a Senior Manager, AI Deployment within the AI Foundations team, you will lead the strategy and execution of model performance and on-vehicle inference for autonomous driving systems. You will manage engineering managers and technical leaders to oversee model optimization, GPU systems, and vehicle integration while establishing performance budgets for latency, memory, and power. Your daily work involves investigating bottlenecks in kernels and scheduling, building automated benchmarking tools, and ensuring numerical parity across simulation and hardware environments. The role requires expertise in PyTorch, CUDA, C++, Python, and TensorRT to optimize complex autonomy models. You will navigate technical trade-offs between accuracy and performance while collaborating with cross-functional teams to ensure production readiness. This position solves critical challenges in making machine learning models faster, more efficient, and reliable on production vehicle hardware for next-generation autonomous driving technology.
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, and hardware utilization.
Establish repeatable benchmarking and profiling practices across simulation, hardware-in-the-loop, bench, and vehicle environments.
Build performance dashboards, regression detection systems, and automated root-cause diagnostics.
Translate profiling results into actionable recommendations for model architects and research teams.
Manage the hiring, coaching, and development of a high-performing engineering organization.
Define and manage KPIs for inference metrics and resolve cross-functional trade-offs regarding performance and quality.
What we're looking for
Bachelor's degree in Computer Science, Electrical or Computer Engineering, Robotics, Machine Learning, or a related field; advanced degree 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 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 factors determining model performance, such as architecture, tensor shapes, operators, kernels, memory movement, and hardware utilization.
Hands-on experience with PyTorch, CUDA, C++, Python, TensorRT, GPU profiling, benchmarking, 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.
TensorRT
CUDA
C++
Python
PyTorch
ONNX
Triton
Deep Learning Inference
Model Optimization
Quantization
Pruning
Linux
ISO 26262
QNX
Real-time Systems
DriveOS
Hypervisors