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Corporate:
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Light \& Wonder’s corporate team is comprised of incredible talent that works across the enterprise, defying boundaries to provide essential services in an extraordinary manner to ensure the success of the organization and the well\-being of employees.
Position Summary
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Light \& Wonder is seeking an experienced Senior Counsel, AI and Data Governance to join its global Legal Department. Reporting to and working closely with the Associate General Counsel for Data Privacy and Employment, this role carries broad responsibility for supporting the Company’s global AI governance program.
The ideal candidate brings relevant experience in technology law and AI, sharp legal instincts, sound business judgment, and the ability to translate complex legal issues into clear, actionable guidance for management. We are looking for someone who leads with practical solutions and collaborates effectively with colleagues across the global Legal Department and the business.
*Key Responsibilities \& Outcomes:*
AI Governance \& Legal Advisory
- Serve as legal advisor on the Company's enterprise\-wide adoption, deployment, and use of AI and machine learning tools, including generative AI platforms and AI\-enabled products.
- Monitor, analyze, and advise on the rapidly evolving global AI regulatory landscape, including the EU AI Act, US federal and state AI legislation, and emerging frameworks across key jurisdictions (UK, Canada, Brazil, India, Australia, Singapore/APAC).
- Support the Company's AI governance framework, including internal policies, standards, and acceptable use guidelines governing the procurement, deployment, and employee use of AI tools.
- Assess and classify AI systems in accordance with applicable regulatory risk tiers (e.g., EU AI Act high\-risk classifications) and advise product, engineering, and business teams on applicable compliance obligations.
- Provide legal guidance on AI\-specific risks including algorithmic bias and discrimination, risks related to AI use in connection with employment and transparency obligations.
- Work with the Legal Department’s IP team on risks related to IP ownership issues arising from AI\-generated outputs.
Policy Development \& Internal Governance
- Maintain Company\-wide policies and standards governing AI use, data governance, digital accessibility, and technology risk, in collaboration with Legal, IT, Information Security, HR, and business stakeholders.
- Support the development and delivery of training programs to educate employees on responsible AI use, data privacy obligations, and related compliance requirements.
- Support the building and maintaining of internal governance structures for AI oversight, including cross\-functional working groups, risk review processes, and executive reporting frameworks.
- Advise on AI\-related employment law issues, including the use of AI in hiring, performance management, workforce planning, and employee monitoring.
AI \& Technology Contract Review
- Negotiate and draft enterprise AI tool agreements, SaaS contracts, and data processing agreements, with a focus on protecting the Company's rights in inputs and outputs, preventing vendor use of Company data for model training, ensuring robust IP indemnification, and negotiating appropriate liability frameworks.
- Develop and maintain internal playbooks, standard positions, and clause libraries for AI\-related contract provisions, including IP ownership, data security, model change notification, acceptable use, audit rights, and third\-party model disclosures.
- Review and advise on vendor sub\-processor chains and foundation model dependencies to assess downstream legal risk.
- Advise on the accessibility and digital inclusion obligations associated with AI\-powered products and platforms, including compliance with WCAG standards and applicable ADA/EAA requirements.
Risk Management \& Regulatory Affairs
- Identify, assess, and advise on legal and regulatory risks arising from the Company's AI and data practices, and work with cross\-functional teams to develop risk mitigation strategies.
- Track enforcement trends, regulatory guidance, and litigation developments in AI, data privacy, and digital accessibility, and deliver timely legal updates to relevant stakeholders.
- Support government affairs and policy engagement on AI and data governance regulatory developments affecting the Company's business.
- Partner with the Associate General Counsel for Data Privacy and Employment, Information Security and IT teams, as well as the company’s Data Protection Officer, on incident response, regulatory notifications, and remediation efforts related to data breaches, AI system failures, or privacy violations.
Data Privacy \& Protection
- Review and maintain the Company's privacy notice and policy framework, ensuring all employee, applicant, player, and partner\-facing notices accurately reflect current data processing activities — including AI\-enabled processing — and satisfy applicable transparency and disclosure obligations.
- Support data protection impact assessments (DPIAs) and legitimate interest assessments (LIAs) for new products, features, and processing activities, particularly those involving AI and automated processing.
- Draft, review, and negotiate data processing agreements (DPAs), standard contractual clauses (SCCs), and other data transfer mechanisms to support cross\-border data flows.
- Monitor, analyze, and advise on global data privacy law compliance, including GDPR and CCPA.
Global \& Cross\-Functional Support
- Support the Company’s Data Governance COE and provide support and guidance to regional legal colleagues around the world in areas related to AI, data governance and data privacy.
- Support additional projects and initiatives as directed by the Associate General Counsel for Data Privacy and Employment.
Culture, Engagement \& Inclusion
- Champion Light \& Wonder’s Celebrate Perspectives value and support a culture of inclusion and belonging.
- Operate with the highest level of ethics and integrity.
Other
- Limited travel may be required depending on location; Travel estimated at less than 20% of work time.
- Adheres to health \& safety requirements to maintain a safe working environment.
- Performs any other duties as reasonably required.
Qualifications
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Education/Certification
- Juris Doctor (JD) from an accredited law school
- Active membership in good standing in at least one U.S. state bar
Professional Experience
Required
- At least 5 years of relevant experience in technology law with demonstrated focus on AI governance.
- Experience at a national law firm, in\-house, or a combination thereof.
- Experience negotiating enterprise technology and AI agreements.
- Familiarity with AI governance frameworks, the EU AI Act, and US AI regulatory developments.
- Ability to translate complex legal and technical concepts for non\-legal audiences and work effectively across legal, product, engineering, IT, Information Security, HR, and executive stakeholders.
Knowledge and Skills
- Exceptional written and oral communication skills, including the ability to convey complex legal concepts clearly and concisely to business stakeholders.
- Demonstrated ability to exercise sound judgment, maintain a high level of integrity, and deliver practical, business\-oriented legal advice.
- Self\-starter who works independently, takes initiative, and manages competing priorities in a fast\-paced environment.
- Collaborative team player who builds and maintains strong relationships with internal clients, including Information Technology, Information Security, Procurement, HR, and external counterparts.
- Ability to manage workload distribution and contribute to the broader Legal Department.
A\-Player Competencies
- Smart, sharp and concise legal judgment — distills complex issues into clear and actionable guidance.
- Bias for action — provides fast, decisive, and practical legal advice.
- High accountability — owns outcomes, deadlines, and quality without excuses.
- Strong relationship builder — earns trust with internal clients, Legal colleagues, and management.
- Handles pressure — thrives in a fast‑paced, highly regulated environment.
- Builder mindset — improves processes, creates clarity, and operationalizes legal guidance.
The targeted pay range for this role is $200,000\-$225,000\. The total compensation package for this position may also include applicable incentive compensation, such as an annual performance bonus. Actual compensation packages are based on several factors that may include, but are not limited to skill set, depth of experience, specific work geography, as well as internal equity and alignment with market data.
Physical and Office Requirements:
Office Environment
This job may be located at a Light \& Wonder US office. The Light \& Wonder Legal Department requires a minimum of four days per week in the office. Remote location may be considered for exceptional candidates.
*Light \& Wonder and its affiliates (collectively, “L\&W”) are engaged in highly regulated gaming and lottery businesses. As a result, certain L\&W employees may, among other things, be required to obtain a gaming or other license(s), undergo background investigations or security checks, or meet certain standards dictated by law, regulation, or contracts. In order to ensure L\&W complies with its regulatory and contractual commitments, as a condition to hiring and continuing to employ its employees, L\&W requires all its employees to meet those requirements that are necessary to fulfill their individual roles. As a prerequisite to employment with L\&W (to the extent permitted by law), you shall be asked to consent to L\&W conducting a due diligence/background investigation on you.*
*This job description should not be interpreted as all\-inclusive; it is intended to identify major responsibilities and requirements of the job. The employee in this position may be requested to perform other job\-related tasks and responsibilities than those stated above.*
\#LI\-JM1
Light \& Wonder is an Equal Opportunity Employer and does not discriminate against applicants due to race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. If you’d like more information about your equal employment opportunity rights as an applicant under the law
Salary Context
This $200K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Light & Wonder, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($212K) sits 17% above the category median. Disclosed range: $200K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Light & Wonder AI Hiring
Light & Wonder has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Las Vegas, NV, US. Compensation range: $225K - $225K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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