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Job Description
GM is seeking an AI \& Data Governance Counsel to join the team and help build, operationalize, and scale the company’s AI governance program as enterprise AI adoption accelerates across products, services, internal tools, and business workflows. This lawyer will work across product\-facing, governance\-facing, and policy\-adjacent matters, with a primary focus on high\-risk and high\-visibility AI initiatives, the AI impact assessment workflow, AI inventory, governance controls and mitigations, transparency artifacts, and cross\-functional enablement. The role is designed for a lawyer who can combine strong legal judgment with practical execution, operate effectively across multidisciplinary teams, and help GM lead rather than follow in AI governance as we deploy AI assets across all facets of the business and the vehicles.
Key responsibilities
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- Provide legal counsel on GM’s highest\-risk and highest\-visibility AI use cases, products, and governance escalations to ensure safe\-by\-design development and alignment with internal safety standards.
- Partner closely with Security, DevEx, Data Governance, Enablement, product teams, and other cross\-functional stakeholders to define internal AI safety standards and scale and operationalize AI guardrails, controls, and mitigation frameworks.
- Help scale and refine GM’s AI impact assessment process, including policy as code, issue spotting, risk triage, mitigation mapping, and governance decision\-making for higher\-risk AI uses.
- Review and negotiate strategic contracts for AI solutions and AI add\-ons, including evaluating other companies’ AI governance maturity.
- Develop external\-facing AI usage policies, post\-deployment monitoring standards, enforcement mechanisms, supporting materials, and data\-logging practices to improve quality metrics and strengthen external defensibility.
- Support the buildout and maintenance of GM’s AI inventory and related governance documentation, including links to assessments, ownership, and risk categorization.
- Advise on AI transparency and documentation artifacts, including explainability frameworks, AI system cards for consumer\-facing AI products, and safety\-aligned AI artifacts where appropriate.
- Translate emerging legal, regulatory, and standards developments into practical internal guidance, workflows, and product\-facing requirements.
- Drive alignment on AI safety strategy and risk posture as it relates to AI deployed in physical spaces and machines.
- Help develop and socialize repeatable AI governance practices, training materials, and legal guidance that scale expertise across legal, product, and business teams.
- Support governance forums and cross\-functional operating structures, including the AI Council, AI governance working groups, and the AI Virtual Team.
- Contribute to external AI governance engagement with peer companies, standards bodies, and industry groups to help GM stay ahead of evolving norms and expectations.
Required qualifications
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- J.D. and active bar membership in good standing.
- 8\+ years of experience counseling on AI/ML, robotics, technology, digital products, data use, privacy, cybersecurity, or related regulatory matters.
- AI fluency, including complex prompting, agent development, and vibe\-coding, including knowledge of AI fitness for purpose and its limitations.
- Demonstrated familiarity with global AI legal and standards frameworks and the ability to apply them in practical business and product contexts.
- Proven track record of building 0\-1 systems and scaling governance programs, workflows, or controls rather than only advising on them.
- Strong judgment in ambiguous, fast\-moving, cross\-functional environments.
- Experience working across multidisciplinary teams to implement governance, compliance, or risk\-management practices in real operating processes.
- Ability to communicate clearly with legal, technical, product, compliance, and business stakeholders.
- Ability to move fluidly between strategic governance questions and hands\-on execution work.
- Experience drafting, interpreting, and negotiating contracts and templates for deals involving AI solutions and add\-ons.
Preferred qualifications
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- Experience driving AI governance, responsible AI, algorithmic accountability, model risk, AI evaluations, and red\-teaming.
- Experience building solutions with the help of AI.
- Familiarity with enterprise AI products, transparency artifacts, product documentation, or safety\-aligned governance frameworks.
- Experience engaging with external industry groups, benchmarking forums, or standards\-development efforts related to AI, privacy, or digital governance.
- Ability to identify skills gaps, create practical guidance, and help mature internal governance capabilities across business functions.
Location: This role is based remotely but if you live within a 50\-mile radius of (Atlanta, Austin, Detroit, Warren, Milford or Mountain View), you are expected to report to that location three times a week, at minimum.
Relocation: This role is NOT eligible for relocation benefits
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., H\-1B, OPT, STEM OPT, CPT, TN, J\-1, etc.)
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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/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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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. Positions span Sunnyvale, CA, US, Mountain View, CA, US, Remote, US. Compensation range: $198K - $331K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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