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About This Role
Job Description
About the role
General Motors is building one of the most ambitious AI and robotics programs in manufacturing, developing systems capable of autonomously manipulating thousands of parts across GM’s global plant network. Our Robotics AI organization sits at the intersection of AI research, robotics, manufacturing engineering, and AI product management, driving both cutting‑edge technology and real factory impact.
We are looking for a Robotics AI Strategy & Program Lead who can own end‑to‑end, cross‑functional initiatives that cut across AI research, robotics engineering, data infrastructure, and manufacturing operations. They will be known as an operator who gets things done across complex organizations.
This role blends strategy, program management, analytics, and hands‑on AI tools work : you will structure the problems, build the dashboards, dive into the data, leverage and build AI agents, and drive large programs to completion while unblocking teams across IT, Purchasing, Vendors, Finance, HR, and Manufacturing.
You will be embedded with AI researchers, roboticists, AI engineers, data engineers, and AI product managers , translating between technical and non‑technical stakeholders and ensuring that what we build is strategic, measurable, and shippable - from fast-turnaround experiments to multi-year platform bets.
What you will own
Program and project leadership
- Own end‑to‑end cross‑functional projects from problem framing and strategy through execution, measurement, and iteration.
- Program manage large, multi‑team initiatives (e.g., new robotics AI capabilities, data/telemetry programs, analytics or tool rollouts) with clear milestones, owners, and success metrics.
- Build and maintain program roadmaps, dependency maps, and risk registers; proactively identify and resolve blockers across teams.
- Drive operating rhythms (standups, reviews, steering forums) with AI Research, AI Engineering, Robotics Engineering, Manufacturing Engineering, and AI Product Management.
- Roll up your sleeves to push work through organizational and process complexity—coordinating with IT, Purchasing, Vendors, Finance, HR, and other partners to keep programs moving.
Comfortably manage multiple initiatives at once, switching contexts between short-turnaround deliverables and long-horizon programs without dropping details
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Strategy, storytelling, and decision support
- Partner with leadership to define and refine Robotics AI strategy, including problem selection, sequencing of bets, and success metrics.
- Structure clear narratives and storyboards that connect technical work to GM‑level business outcomes and manufacturing KPIs.
- Prepare executive‑ready presentations and decision docs (SVP and above), highlighting what has changed, what is at risk, and what decisions are needed.
Translate complex technical options into simple, structured trade‑off frameworks for senior decision‑makers.
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Data, analytics, and tooling
- Go deep on data: define questions, shape logging/telemetry requirements, pull and analyze data using SQL, Python, or R, and translate findings into actionable insights.
- Build and maintain dashboards (e.g., Looker, Power BI) that track program KPIs, model and system performance, factory deployment health, and operational metrics.
- Partner with Data Engineering and platform teams to leverage Databricks and other infrastructure for analytics and reporting.
- Design and orchestrate GenAI‑powered workflows and agents to automate repetitive analysis, reporting, and operational tasks where appropriate.
Continuously learn and adopt new AI, data, dashboarding, and tracking tools, and help the broader team do the same
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AI, robotics, and technical collaboration
- Act as a fluent technical partner to AI researchers, robotics engineers, and data engineers—you don’t need to write production code or models, but you do need to understand what makes them hard.
- Help ensure that data collection, labeling, evaluation, and deployment workstreams are aligned with program goals and success metrics.
- Collaborate closely with AI product managers to connect user needs, technical capabilities, and deployment constraints, especially in manufacturing settings.
Engage with external partners and vendors in AI, robotics, and data tooling where both technical depth and program context are required.
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Ways of working and culture
- Champion a learning‑oriented culture: rapidly explore new tools (GenAI, agents, analytics, low‑code platforms), techniques, and methodologies, and help the team adopt what works.
- Model “get your hands dirty” execution—whether that’s pulling data, building a quick dashboard, prototyping an AI agent, or refining a deck the night before an exec review.
- Help continuously improve our program management, measurement, and communication practices across the Robotics AI org.
- Practice proactive, “over-communication” with leaders and stakeholders—keeping partners up to speed on risks, decisions, and progress, and responding quickly to questions or asks.
Thrive in a dynamic environment where priorities can shift, and enjoy moving between strategic problem framing, detailed data work, and day-to-day execution.
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Required qualifications
- MBA with previous engineering or technical experience or Master's in Computer Science, Electrical Engineering, Mechanical Engineering, or a closely related technical field.
- 2-4 years of experience in one or more of:
- Management consulting or corporate strategy
- Product management or technical program management
- Data science or data analytics
- Meaningful experience in AI, ML, or robotics (e.g., robotics AI, applied ML, computer vision, intelligent automation, or similar), ideally at the intersection of AI and robotics.
- Demonstrated ability to lead large, complex cross‑functional programs with high ambiguity and multiple parallel workstreams.
- Strong strategic thinking and storytelling skills: can frame problems, structure options, and communicate clearly to both technical and non‑technical audiences.
- Comfort operating as a hands‑on analyst and builder, including:
- Writing SQL and using Python or R for analysis
- Building dashboards in tools like Looker or Power BI
- Working in Databricks and similar data platforms
- Demonstrated fluency with GenAI tools (e.g., prompt engineering, using copilots/assistants in daily workflows) and a track record of designing or orchestrating AI‑assisted workflows or agents.
- Experience with modern collaboration and delivery tooling such as Jira and low/no‑code application platforms (e.g., Power Apps, AppSheet).
- Ability to modulate communication style: comfortable diving into technical details with AI researchers and roboticists, and equally comfortable presenting synthesized insights to senior, non‑technical executives.
- Proven cross‑functional collaboration and influence skills; known as a team player who creates clarity and momentum rather than just tracking tasks.
Demonstrated experience working directly with technical teams (AI researchers, AI engineers, roboticists, data engineers), understanding their work well enough to track progress, surface risks, and offer hands-on support
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Preferred Qualifications
- Prior work in robotics, robotics AI, or physical AI systems, especially in manufacturing, industrial, or logistics environments.
- Experience operating at the intersection of research and production, helping teams balance exploration with delivery.
- Background in data‑driven decision‑making, experimentation, and KPI design in complex technical domains.
- Experience defining and instrumenting metrics for AI or robotics systems (e.g., reliability, coverage, cycle time, throughput, data quality).
- Comfort with storyboarding and narrative design—you naturally think in terms of “what’s the story?” and how to land it in a deck or memo.
- Track record of rapidly learning new technical domains and tools and helping teams adopt them effectively.
Experience driving programs through enterprise processes (e.g., security reviews, procurement, vendor onboarding) and removing organizational blockers to keep technical teams moving.
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Who you are
- Strategic and big‑picture thinker who can also go deep into the data and technical details when needed, and who enjoys switching between high-level framing and hands-on execution.
- Storyteller and storyboarder who can craft clear narratives from complex technical work, elevating the message for SVP-level audiences while making it appropriately technical for AI researchers and engineers.
- Cross‑functional collaborator who builds trust with engineers, researchers, product leaders, and manufacturing partners, and is known as an operator/hustler who gets things done across organizational boundaries.
- Curious, hungry learner of new tools, techniques, and subject areas; you proactively experiment with new AI and analytics capabilities, and quickly incorporate them into how you and the team work.
- Hands‑on and detail‑oriented , willing to get into the weeds on data, tools, and process to make programs successful, from small, fast-turnaround analyses to multi-quarter initiatives.
- Technically fluent , able to understand AI and robotics concepts well enough to ask the right questions and bridge between disciplines, and comfortable interfacing directly with AI researchers, AI engineers, and roboticists on a daily basis.
A proactive, “over-communicating” operator who keeps stakeholders informed, surfaces risks early, and follows through reliably - especially when juggling multiple priorities at once
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Location
- 5x days on-site
- Primary location: Warren, MI
- You will sit within GM’s AI Product Management and Strategy organization, working directly with the Robotics AI team and partnering closely with AI Research, AI Engineering, Robotics Engineering, Data Engineering, and Manufacturing Engineering.
This is a high‑visibility role with regular exposure to senior leadership and key external technology partners.
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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.
- Compensation: The expected base compensation for this role is : $83,800 - $127,300. 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.
Relocation benefits are available for candidates who qualify under company policy.




 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.
Salary Context
This $83K-$127K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At General Motors (GM), this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($105K) sits 50% below the category median. Disclosed range: $83K to $127K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
General Motors (GM) AI Hiring
General Motors (GM) has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Engineer, Data Scientist. Positions span Warren, MI, US, Milford, MI, US, Mountain View, CA, US. Compensation range: $127K - $290K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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