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About This Role
Job Description
Role Summary
The Privacy \& AI Operations Manager at General Motors drives execution, governance maturity, education, metrics, and continuous improvement for GM’s enterprise\-wide Privacy program. This operational leader will help scale GM’s privacy and AI governance capabilities through clear workflows , measurable outcomes, stakeholder engagement, process optimization, and organizational awareness.
Unlike roles focused on Data Subject Requests (DSRs) and daily workflows, this position oversees the broader health of GM’s privacy program and emerging AI governance efforts. It leads reporting frameworks, training and ed u cation , governance processes, policy management , and strategic initiatives that help GM maintain trust while meeting evolving regulatory, business, and customer expectations . The role will help turn privacy and AI governance into coordinated, measurable, and scalable enterprise program s .
The successful candidate will be highly organized, analytical, and skilled at driving execution across complex, global, cross\-functional teams. They will demonstrate a proven ability to manage competing priorities, navigate ambiguity, and build alignment among diverse stakeholders to achieve strategic business objectives .
In addition, the ideal candidate will have experience leveraging artificial intelligence and emerging technologies to enhance efficiency, streamline processes, and improve decision\-making. They will possess a strong understanding of how AI can be applied to optimize workflows, automate routine tasks, uncover insights from data, and enable teams to work more effectively.
Core Responsibilities
1\. Lead Enterprise Privacy \& AI Governance Operations
- Drive the day\-to\-day operational management of GM’s Privacy and AI governance programs, e stablish ing sustainable processes, operating rhythms, and oversight mechanisms
- Coordinate privacy governance forums (e.g., Privacy Champions Program and International Privacy CoE ), ensuring clear agendas, crisp materials, decision tracking, and follow\-through on action items
- Support risk assessment development and other regulatory readiness activities
- Monitor program effectiveness and identify opportunities to strengthen governance maturity
- Ensure governance processes remain aligned to regulatory obligations, industry best practices, and evolving business needs
- Support implementation and operationalization of new privacy and AI governance frameworks, controls, and initiatives
2\. Lead Privacy \& AI Training, Awareness \& Education Programs
- Own the strategic development and execution of GM’s Privacy and AI training and awareness programs.
- Develop annual training plans aligned to organizational needs and emerging risks.
- Create role\-based education programs tailored to varying audiences across the organization.
- Design and deliver awareness campaigns that promote responsible data use and trustworthy AI practices.
- Develop executive briefings, learning materials, awareness assets, guidance documents, and communications.
- Measure training effectiveness and continuously improve educational offerings.
- Partner with HR, Communications, Learning \& Development, and functional stakeholders to expand program reach and impact.
3\. Own Enterprise Privacy Health Metrics \& Executive Reporting
- Develop and maintain a unified Privacy \& AI program measurement framework.
- Consolidate key health indicators into a executive ready da shb oards and reports.
- Develop reporting methodologies, establishing KPI definitions, performance thresholds, reporting cadences, and governance metrics.
- Analyze trends, risks, gaps, and opportunities and provide actionable insights and recommendations to senior leadership.
4\. Drive Privacy Program Documentation \& Content Management
- Lead creation, maintenance, and publication of all Privacy Operations program materials.
- Establish governance and ownership processes for enterprise privacy content.
- Ensure documentation remains current, accurate , and aligned with regulatory and business requirements.
- Partner with Legal and Communications teams to support publication and communication of updates.
5\. Support Responsible AI Governance Operations
- Partner with AI G overnance to operation alize the evolving governance framework , and document and systematize each process as it stabilizes . This is a build\-phase operations role, not the maintenance of a finished machine .
- Coordinate the AI Impact Assessment workflow end \- to \- end operationally and help formalize and document the workflow as its design matures.
- Help to administer the new AI governance platform , leading workflow configuratio n and user / reviewer role management .
- Own program metrics and reporting: build and maintain risk dashboards and recurring status reporting to the AI Governance team, AI Working Group, the Privacy \& AI Council, and leadership.
6\. Drive Operational Excellence \& Continuous Improvement
- I dentify opportunities to simplify, automate, streamline, and scale privacy and governance operations using AI and other emerging technologies
- Lead strategic operational initiatives that improve efficiency, transparency, and program maturity.
- Develop future\-state processes and operating models that reduce manual effort and strengthen governance outcomes.
- Partner with technology teams to improve reporting tools, workflows, automation, and user experiences.
Required Qualifications:
- Bachelor's degree
- 7\+ years of experience in Privacy, Risk Management, Data Governance, Operations, Program Management, or related fields
- Experience leading enterprise\-wide programs with cross\-functional stakeholders
- Strong executive communication and presentation skills
- Experience developing metrics, dashboards, reporting frameworks, and governance processes
- Ability to translate complex regulatory or technical concepts into actionable business guidance
- Strong project management, organizational, and stakeholder management skills
Preferred Qualifications:
- Privacy or AI certifications (CIPP, CIPM, CIPT, AIGP or equivalent).
- Previous people management experience preferred.
- Experience supporting AI governance, digital trust, responsible AI, or emerging technology programs.
- Experience with privacy and consent management platforms ( TrustArc or similar).
- Experience with Power BI, reporting platforms, or data visualization tools.
- Experience working in highly regulated or global environments.
- Familiarity with privacy regulations including U.S. state privacy laws, GDPR, LGPD, PIPEDA, the EU AI Act, and other global privacy and AI governance frameworks.
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 actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
- The salary range for this role is $112,000\-$175,000 . 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
\#LI\-HP2
\&\#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;This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.\&\#xa;\&\#xa;This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.\&\#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.
Salary Context
This $112K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($143K) sits 33% below the category median. Disclosed range: $112K to $175K.
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.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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).
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 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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