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Role: Director, Corporate Counsel - Artificial Intelligence
Location: Pittsburgh PA
City, State: Coraopolis, Pennsylvania
Job Area: Full time
Job ID: 202640165
Job Category: Legal
At DICK'S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world's greatest sports team, apply to join our team today!
OVERVIEW:
Role Responsibilities:
The Director, Corporate Counsel - Artificial Intelligence will help DICK'S Sporting Goods develop and use artificial intelligence responsibly to improve experiences for athletes and teammates. The role will primarily focus on AI governance and product counseling, with additional responsibility for AI-related contracting support in partnership with Commercial Legal.
This attorney will translate emerging legal and regulatory requirements into practical business guidance, evaluate AI use cases, and help teams incorporate appropriate safeguards into products and internal tools. The role will manage routine matters within established guidance, while partnering with senior counsel and designated governance reviewers on novel, higher-risk, or material issues.
This is an opportunity to build substantive AI counseling experience while working closely with colleagues across product, engineering, data science, business, Legal, Privacy, Security, Intellectual Property, Regulatory, Procurement, and Commercial Legal. Prior AI-specific legal experience or certification is not required. The ideal candidate will bring sound judgment, strong communication skills, technical curiosity, and a willingness to learn.
Key Responsibilities
AI Regulatory and Product Counseling
- Monitor and analyze U.S. and international AI legal and regulatory developments relevant to DICK'S Sporting Goods and translate them into clear business implications and practical recommendations.
- Advise product and business teams on AI-enabled products and internal tools throughout the full lifecycle, including ideation, design, testing, launch, monitoring, and retirement.
- Assess legal risks involving generative AI, agentic AI, and other AI and machine-learning systems.
- Recommend proportionate safeguards, disclosures, and human oversight measures that support responsible AI use.
- Partner with Privacy, Security, Intellectual Property, Regulatory, and other legal specialists on data practices, consumer protection, and the accuracy and substantiation of AI-related claims.
AI Governance and Risk Assessment
- Evaluate proposed AI use cases using approved governance frameworks and conduct initial legal risk and impact assessments.
- Recommend appropriate risk mitigations and support AI intake, inventory, and classification processes.
- Work with technical and business teams to understand intended uses, data flows, system limitations, and existing safeguards.
- Present clear legal analysis and recommendations to senior counsel and designated governance reviewers.
- Translate governance decisions into actionable conditions and follow-up requirements.
- Track implementation and identify material changes in use, data, or system capabilities that require additional review.
- Coordinate governance reviews with Procurement and Commercial Legal to ensure required approvals and contractual protections are addressed before implementation.
AI Contracting Support and Data Rights
- Assess AI-specific vendor diligence findings and support the review of AI-related technology agreements in partnership with Commercial Legal and Procurement.
- Draft and negotiate AI-specific contractual provisions under approved positions and senior counsel guidance.
- Address issues involving permitted uses, model training, data rights, confidentiality, output ownership and use, security, warranties, indemnification, and risk allocation.
- Evaluate data sourcing, licensing, and reuse rights in partnership with legal specialists, including potential privacy, copyright, intellectual property, and open-source requirements.
- Translate diligence findings and governance conditions into contractual protections and implementation guidance.
- Escalate material departures from approved positions for senior review.
Cross-Functional Partnership and Practice Development
- Build trusted relationships across functions and translate technical concepts and legal requirements into clear, business-ready guidance.
- Develop and refine AI policies, templates, playbooks, launch checklists, and training materials in partnership with senior counsel.
- Use recurring issues and emerging trends to improve practical guidance and repeatable legal processes.
- Manage assigned matters and balance legal analysis with intake, documentation, and follow-up responsibilities.
- Identify competing deadlines, unresolved risks, and matters requiring specialist or senior-level input.
Experience
- Three to five years of relevant legal experience in one or more of the following areas: Technology transactions Product counseling Data privacy Intellectual property Consumer protection A related technology-focused legal practice
- Experience may come from a law firm, an in-house legal department, or a combination of both.
- Experience supporting retail, e-commerce, consumer-facing digital products, software, or SaaS businesses is preferred but not required.
- Experience collaborating with technical and cross-functional teams is preferred.
- Exposure to AI use cases, governance reviews, product counseling, or AI-related technology agreements is preferred but not required.
Knowledge, Skills, and Abilities
- Genuine curiosity about how AI systems are developed and used, with the ability and willingness to learn technical concepts and emerging legal requirements.
- Strong legal research, drafting, analytical, and communication skills.
- Ability to ask effective technical questions and translate legal analysis into clear, practical recommendations.
- Sound business judgment and the ability to manage and prioritize multiple concurrent matters.
- Ability to independently manage routine work within established guidance and recognize when senior review or specialized legal input is needed.
- Strong collaboration skills and the ability to work effectively with lawyers, technical teams, and business partners.
- Ability to embrace diverse perspectives and adapt within a fast-paced, evolving environment.
Preferred Qualifications
- AI or privacy training or certification, such as the IAPP Artificial Intelligence Governance Professional or Certified Information Privacy Professional designation.
- Experience with AI governance, AI-related technology agreements, or the legal review of AI-enabled products and internal tools.
- Prior AI-specific legal practice, AI or privacy certifications, and an engineering or coding degree are not required.
#LI-CHK1
QUALIFICATIONS:
- Advanced Degree
- JD Degree
- 3-5 years of experience
- Experience practicing law, preferably in a law firm or corporate legal department.
VIRTUAL REQUIREMENTS:
At DICK'S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.
To ensure a smooth and secure experience, please note the following:
- Cameras must be on during all virtual interviews.
- AI tools are not permitted to be used by the candidateduring any part of the interview process.
- Offers are contingent upon a satisfactory background check which may include ID verification.
If you have any questions or need accommodations, we're here to help. Thanks for helping us keep the process fair and secure for everyone!
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 DICK'S Sporting Goods, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
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.
DICK'S Sporting Goods AI Hiring
DICK'S Sporting Goods has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Coraopolis, PA, US.
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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