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Job Description
The Role
At General Motors, we empower P roduct M anagers to solve challenging customer and business problems. We seek passionate and innovative team members who can collaborate effectively within product management, program management, design, and engineering teams to discover and deliver impactful solutions. We hold our teams accountable for results and seek leaders who can influence teammates, stakeholders, and executives using data and logic.
As a Staff Product Manager y ou will define and drive the product strategy, requirements, and execution priorities for the systems that enable large\-scale model training, experimentation, evaluation, and developer productivity across GM’s autonomous vehicle platform. This role will own critical product surfaces and workflows that help machine learning engineers, data scientists, and autonomy teams prepare training data, configure experiments, monitor progress, evaluate outcomes, and improve iteration speed.
Success requires strong technical judgment, deep customer empathy for ML practitioners, and the ability to translate complex training workflow needs into clear product requirements. You should be comfortable partnering closely with engineering, infrastructure, data, finance, and autonomy stakeholders; making principled tradeoffs across velocity, cost, reliability, and model quality; and influencing without direct authority in a highly technical environment.
What You’ll Do
- Own product r oadmap and execution for AI/ML training workflow capabilities that improve model development speed, training reliability, experiment traceability, and developer productivity.
- Deeply understand the end\-to\-end ML training lifecycle, including data selection , dataset preparation, training job configuration, orchestration, monitoring, evaluation, debugging, and deployment handoffs.
- Act as the voice of ML engineers , data scientists, autonomy developers, and infrastructure users by creating and running pain point intake loop , identifying workflow friction, productivity bottlenecks, and opportunities to reduce cycle time. Own framework to stack\-rank and convert them into prioritized product requirements.
- Define product requirements for training platforms, developer tools, observability systems, workflow automation, experiment management, and performance reporting.
- Drive a metrics\-based approach to product decisions by defining KPIs for developer productivity, training throughput, cost efficiency, experiment success rates, and time\-to\-insight.
- Prioritize product investments by balancing customer impact, engineering complexity, infrastructure cost, model quality impact, and business urgency.
- Fund unglamorous reliability and platform tech debt against competing demand for visible features, and of articulating that tradeoff to senior leaders in terms of throughput and cost beyond engineering hygiene.
- Collaborate with engineering, program management, design, data platform, compute infrastructure, and finance teams to deliver high\-impact capabilities on predictable timelines.
- Use data, user research, workflow analysis, and internal benchmarking to inform roadmap decisions and validate whether shipped capabilities improve developer experience and productivity.
- Communicate product status, tradeoffs, risks, and recommendations clearly to senior leaders, technical stakeholders, and cross\-functional partners.
- Mentor other product managers and cross\-functional partners through technical product best practices, without direct people\-management responsibility.
- Stay current on AI/ML platform trends, developer productivity tooling, model training infrastructure, and competitive approaches to large\-scale ML operations.
Your Skills \& Abilities (Required Qualifications)
- 8\+ years of product management or related technical product experience, including ownership of complex software platforms or developer\-facing products.
- Experience building products for AI/ML, data science, developer productivity, infrastructure, platform engineering, or other highly technical users.
- Proven ability to define product vision, strategy, requirements, and success metrics for complex software products from concept through delivery and iteration.
- Strong understanding of the ML lifecycle, including data pipelines, model training, experimentation, evaluation, performance analysis, and production handoffs.
- Strong analytical skills with the ability to use quantitative and qualitative evidence to identify workflow bottlenecks, prioritize investments, and measure product impact.
- Technical proficiency in working with complex software systems, distributed workflows, cloud or compute infrastructure, and data\-intensive products . You must be able to independently reason for distributed training failures, like checkpoint recovery, GPU utilization loss, job variance, orchestration failures, and hold a reasonably technical debate with an ML engineer.
- Excellent written and verbal communication skills, including the ability to explain technical concepts, tradeoffs, and recommendations to both technical and non\-technical partners.
- Demonstrated ability to partner with and influence senior stakeholders and cross\-functional teams without direct reporting authority.
- Comfort operating in ambiguous, fast\-moving technical environments and making clear tradeoffs across speed, quality, cost, reliability, and user experience.
- High ownership, resilience, and curiosity, with a track record of turning complex customer and engineering problems into practical product outcomes.
What Will Give You a Competitive Edge (Preferred Qualifications)
- Master’s or Doctorate degree in computer science, engineering, data science, machine learning, or a related technical field.
- Experience working directly with machine learning engineers, data scientists, or research teams on training platforms, experimentation systems, model evaluation workflows, or MLOps tools.
- Experience improving developer productivity through workflow automation, observability, self\-service tooling, platform simplification, or internal product development.
- Experience with autonomous vehicles, robotics, simulation, perception , planning, or other data\-intensive AI systems.
- Experience driving change in large, complex organizations while partnering across product, engineering, infrastructure, finance, and program management teams.
Hybrid: This role is categorized as hybrid . This means the successful candidate is expected to report to the Warren Technical Center in Warren, MI or Sunnyvale, CA office three times per week, at minimum.
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 the California Bay Area.
The salary range for this role is $134,700 to $245,00\. The actual base salary a successful candidate will be offered within this 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, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.
\#LI\-RF1
\#GM\-AV\-1
\&\#xa;\&\#xa;GM does not provide immigration\-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1\-B, OPT, STEM OPT, CPT, TN, J\-1, etc).\&\#xa;\&\#xa;\&\#xa;\&\#xa;
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.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At General Motors (GM), this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Motors (GM) AI Hiring
General Motors (GM) has 13 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Warren, MI, US, Austin, TX, US, Sunnyvale, CA, US. Compensation range: $173K - $335K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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