Senior AI Deployment Leader: Real-Time Inference

General Motors; Sunnyvale, Northern (CA, KY); Hybrid; USD 296,000 - 454,000; Full time

Job summary

General Motors is seeking a Senior Manager, AI Deployment to lead strategy and execution of model performance and on-vehicle inference for autonomous driving. You will guide optimization across model architecture, operators, kernels, and hardware-aware execution, building dashboards and collaborating with cross-functional teams.

You will manage a team of engineering leaders, define KPIs for latency, throughput, and memory efficiency, and balance near-term needs with longer-term investments in

Qualifications

• 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 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.

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

PyTorch

CUDA

C++

Python

TensorRT

GPU profiling

Benchmarking

Performance analysis

Inference runtimes

Education

Bachelor's degree in CS/EE/CE/Robotics

Advanced degree preferred

Tools

PyTorch

CUDA

C++

Python

TensorRT

Job description

General Motors is seeking a Senior Manager, AI Deployment to lead strategy and execution of model performance and on-vehicle inference for autonomous driving. You will guide optimization across model architecture, operators, kernels, and hardware-aware execution, building dashboards and collaborating with cross-functional teams.

You will manage a team of engineering leaders, define KPIs for latency, throughput, and memory efficiency, and balance near-term needs with longer-term investments in