# General Motors — Senior Manager, AI Deployment

- Generated: 2026-09-15 10:48:11 PM EDT
- Original/canonical posting: https://wellfound.com/jobs/4704716-senior-manager-ai-deployment
- Provider: Wellfound
- Posted: Not disclosed
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: Not disclosed
- Work model/location: Remote — United States
- Compensation: $296,300-$453,900 base
- Travel: Less than 25%; exceeds Keith's 5% maximum
- Positioning track: Technical manager
- Fit outcome: FAIL — 38%

## Direct-match strengths

Engineering leadership, production AI systems, benchmarking, telemetry, reliability, cross-functional architecture, and performance measurement.

## Hard or material gaps

Hard domain and travel conflicts: the role requires production GPU/accelerator or edge inference, CUDA/TensorRT, quantization and kernel optimization, robotics/autonomous-driving or computer-vision performance, and safety-critical vehicle environments. Travel can approach 25%, above Keith's 5% maximum.

## Evidence map

1. Lead managers and senior technical leaders (weight 3, evidence 3/3) — Led organizations up to 25, including managers and senior engineers.
2. Production GPU and edge inference systems (weight 3, evidence 0/3) — No source-supported CUDA, TensorRT, edge-inference, or GPU-kernel optimization ownership.
3. Autonomous driving, robotics, or computer vision (weight 3, evidence 0/3) — No direct autonomous-driving or robotics deployment experience.
4. Quantization, pruning, distillation, or kernel optimization (weight 3, evidence 0/3) — Unsupported.
5. Benchmarking, telemetry, and regression detection (weight 2, evidence 3/3) — Direct evaluation, observability, performance metrics, and regression practices.
6. Safety-critical vehicle environments (weight 2, evidence 1/3) — Regulated healthcare software is adjacent; vehicle safety systems are not.
7. Travel within 5% (weight 3, evidence 0/3) — Posting requires less than 25%, which may materially exceed 5%.

## Keyword diagnostic

General AI-platform leadership overlaps, but the central GPU/edge/autonomy deployment stack and travel requirement are unsupported or incompatible.

## Full normalized job description

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General Motors
Actively Hiring
We pioneer the innovations that move and connect people to what matters
B2C
Public Stage
Publicly traded company
B2C
Public Stage
Publicly traded company
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Senior Manager, AI Deployment
$296k – $453k
|
Remote (
United States
)
|
10 years of exp
|
Full Time
Posted:
5 days ago
• Recruiter recently active
Hires remotely
in
United States
Remote Work Policy
Remote only
Company Location
Detroit
Visa Sponsorship
Not Available
Relocation
Allowed
Skills
Python
C++
Dashboards
CUDA
Telemetry
Benchmarking
Distillation
performance analysis
PyTorch
TensorRT
Quantization
Pruning
Memory Optimization
Inference Runtimes
Architecture Optimization
Kernel Optimization
GPU Profiling
Benchmark Automation
Performance Regression Detection
Profiling Workflows
About the job
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 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.
What You’ll 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)
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.
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: $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
#GM-AV-1
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. This job may be eligible for relocation benefits.
About GM
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.
Why Join Us
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.
Benefits Overview
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
.
Non-Discrimination and Equal Employment Opportunities (U.S.)
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.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. 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. To learn more, visit
How we Hire
.
Accommodations
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.
Apply Now
About the company
General Motors
Actively Hiring
We pioneer the innovations that move and connect people to what matters
5000+
Employees
Detroit
5000+
Automotive Products
Manufacturing
Software Development
Manufacturing
Automotive
Technology
Mobility
B2C
Public Stage
Publicly traded company
Learn more about
General Motors
Funding
AMOUNT RAISED
$2B
FUNDED OVER
1
round
Round
S
$2000000000
Seed
-
May 2025
View
General Motors
's funding history
Perks
Multi-faceted Healthcare Benefits
Our multi-faceted healthcare benefits includes comprehensive medical benefits plan, a triple tax-advantaged health savings account with GM contribution, plus dental and vision plans.
Retirement
GM offers a competitive 401(k) savings program with both matching and retirement contributions by GM.
GM Paid Maternity Leave
GM Paid Maternity leave offers 6 or 8-weeks following birth (dependent on birth scenario). Employees are also eligible for GM Paid Family Leave (GMPFL), which provides up to twelve (12) weeks paid leave for parents to bond with a new child.
GM Paid Family Leave (GMPFL)
GM Paid Family Leave (GMPFL) provides up to 12 weeks paid leave for parents to bond with a new child or for the employee to care for a spouse, child, or parent who has a serious health condition (this is in addition to paid mat. leave.)
Commitment to Gender Equality
We’re proud to have been recognized as the #1 company for Gender Equality by Equileap who identified as the only leading U.S. company in the S&P 100 Index with no gender pay gap.
Annual Time-Off
We have a generous vacation time policy, which ranges from 3 weeks to up to 6 weeks, based on length of service. Plus approximately 16 annual paid holidays. (U.S. salaried employees)
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General Motors
's perks and benefits
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## Artifact metadata

- Resume: Not generated because the fit gate returned FAIL.
- Cover letter: Not generated because the fit gate returned FAIL.
- LinkedIn note: Not generated for a FAIL role.
- Google Drive used: No