Senior Manager – AI Deployment

🕒 September 10

🏄 California – Remote

🏄 California – Remote

💵 $296.3k - $453.9k / year

⏰ Full Time

🟠 Senior

🤖 Artificial Intelligence

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 18%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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General Motors

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1908

🚘 Automotive

🏭 Manufacturing

🚗 Transport

💰 $500M Grant on 2024-07

Automotive • Manufacturing • Transport

General Motors is a leading multinational corporation in the automotive industry, committed to creating a future with zero crashes, zero emissions, and zero congestion. With operations and facilities worldwide, GM focuses on innovation across various areas including engineering, manufacturing, and information technology. The company emphasizes diversity, inclusion, and the development of sustainable technologies to shape the future of mobility.

📋 Description

• 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 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 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 on performance, quality, and implementation trade-offs • Develop 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 • 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 model-performance factors including architecture, tensor shapes, operators, kernels, memory movement, scheduling, runtime execution, and hardware utilization • Hands-on experience with several of 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 • 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

🏖️ 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 • Relocation benefits available for qualifying candidates • Remote work arrangement • Travel required under 25%