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About This Role
WHO WE ARE
We’re not in the wing business. We’re in the flavor business. It’s been our mission to Serve the World Flavor since we first opened in 1994, and we’re just getting started. 1997 saw the opening of our first brand partner operated Wingstop location, and by 2002 we had served the world one billion wings. It’s flavor that defines us and has made Wingstop one of the fastest growing brands in the restaurant industry.
Above all else – our success is largely due to our people and our core values, or what we call The Wingstop Way of being entrepreneurial, service-minded, fun, and authentic. We believe having a strong people foundation centered on these collective values creates a crave-worthy culture and talented team, as well as ensures our brand is poised for accelerated growth. We all win together.
WHAT WE’LL NEED
Wingstop is seeking a Senior Director of AI Transformation to lead how AI changes the way our business actually works. This is not a strategy-deck role. You will map how work gets done across Operations, Supply Chain, Marketing, Technology, Finance, and Restaurant Support, find where AI creates real leverage, and then drive the redesign and adoption end to end. You will be the translation layer between business and technical teams, fluent enough in the business to know which processes matter, and technical enough to scope solutions credibly with engineering and data science partners. You will build and lead a growing AI transformation team while staying hands on yourself: in the process maps, in the tools, and in the rooms where the work actually changes. This role is based at the Global Support Center in Dallas, TX. Role Responsibilities* Own the enterprise AI transformation roadmap, the sequenced view of which processes we change, in what order, and what value each change delivers
- Lead process discovery and mapping across business functions; document current state and quantify baseline cost, cycle time, and quality
- Identify, size, and prioritize AI use cases against business value, technical feasibility, and change effort
- Translate business requirements into clear, well-scoped technical specifications for engineering, data science, and product teams
- Partner with Applied AI & Data Science and Technology to scope, pilot, and scale solutions, staying close enough to the build to make real tradeoffs
- Design the future-state process, not just the tool, redefine roles, handoffs, controls, and SOPs so the solution sticks after launch
- Build the business case for each initiative and track realized value against it
- Lead change management and adoption: stakeholder alignment, training, communications, and enablement across GSC teams, brand partners, and restaurant teams
- Establish the operating model for AI delivery, intake, prioritization, stage gates, and post-launch measurement
- Set standards for responsible and secure AI use in partnership with Legal, Information Security, and Data Governance
- Hire, develop, and lead a growing team of transformation and process leads while carrying your own workstreams
- Stay current on the AI landscape with models, agents, and tooling. Bring a clear point of view on what is real and what is hype
- Report progress, risks, and value to executive leadership and the Board as needed
WHAT YOU’LL NEED Education Requirements* Bachelor’s degree in Business, Engineering, Computer Science, Industrial Engineering, Economics, or a related field
- MBA or advanced degree is a plus Certifications* Lean Six Sigma, PMP, or change management certification (Prosci) is a plus
- Cloud or AI certifications (AWS preferred) are a plus Tools & Technical Experience* 10+ years spanning enterprise transformation, business operations, or technology delivery, including 3+ years leading teams
- Demonstrated ownership of at least one large-scale enterprise transformation from diagnosis through realized value
- Deep process mapping and redesign experience across multiple business functions (value stream mapping, current/future state, SIPOC, capability modeling)
- Strong working knowledge of modern AI, LLMs, retrieval-augmented generation, agents, and intelligent automatio, and judgment about where each is and is not the right tool
- Hands-on proficiency with AI tooling: frontier model assistants, agentic workflows, prompt and context design, and low-code automation platforms
- Technical depth sufficient to read a solution architecture, question a data model, scope an integration, and hold your own with engineers
- Comfort with SQL and data analysis with able to build your own view of the numbers rather than wait for one
- Experience with cloud data platforms and enterprise systems (ERP, POS, CRM, workforce management, supply chain)
- Track record of building business cases and measuring realized benefit after go-live
- Experience in restaurant, retail, franchise, or other multi-unit operating models is a plus
- Experience standing up an AI or automation center of excellence is a plus
People Skills* Exceptional translator with ability to take a messy business problem and translate to technical teams a clear requirement
- Credible with a brand partner and a staff engineer on the same day
- Executive presence; communicates crisply in writing and in the room
- Influences without authority across functions and levels of seniority
- Builder’s bias - ships pilots, learns, and iterates instead of perfecting the plan
- Leads through change with patience and conviction; expects resistance and works it
- Develops people and raises the capability of everyone around them
WHO YOU ARE
No job is too small. You recognize that the real work happens in the restaurants, and everything we do should support their success. You stay humble, roll up your sleeves, and always look for ways to help. You learn from others and contribute wherever you can.
You care deeply about doing great work and driving results. You’re curious, ask questions, and seek out opportunities to improve. You don’t just point out problems—you bring solutions, ideas, and perspectives that move the team forward.
You take full responsibility for your work and see things through to completion. You aren’t afraid to fail because you know that failure is a part of learning and growth. You take action, move fast, and keep pushing forward.
You lead with empathy, respect, and emotional intelligence. You collaborate effectively, fostering a culture of trust and constructive feedback. You understand the importance of teamwork and ensure that your actions build others up rather than break them down.
You push yourself and others to be better. You embrace healthy conflict, knowing that great ideas and strong teams emerge from honest, constructive conversations. You believe that leaders create leaders and are committed to fostering a culture of growth, challenge, and continuous improvement.
BENEFITS
FLAVOR PERKS:
- Unlimited paid time off for exempt employees
- One paid volunteer day of your choice
- Competitive bonus structure for eligible roles
- Team member stock purchase plan
- Health savings or flexible spending account options
- 401k – (dollar for dollar on the first 3% and then 50 cents on the dollar for the next 2% for team member contributions up to 5% of eligible compensation)
- Comprehensive medical, dental, and vision benefits
- Basic life and AD&D insurance provided
- Pet insurance
- Education Assistance
- Wellness reimbursement program
- Paid maternity and paternity leave
FUN IS THE BEST FLAVOR:
- Lunch provided every Tuesday and Thursday in office
- Discount on Wingstop gift cards
- Onsite game room
Wingstop provides equal opportunities for everyone that works for us and everyone that applies to join our team, without regard to sex or gender, gender identity, gender expression, age, race, religious creed, color, national origin, ancestry, pregnancy, physical or mental disability, medical condition, genetic information, marital status, sexual orientation, any service, past, present, or future, in the uniformed services of the United States (military or veteran status), or any other consideration protected by federal, state, or local law.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wingstop, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wingstop AI Hiring
Wingstop has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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