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About This Role
About Us
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally. The Role
Are you a builder who has already shipped AI that changed how a business runs? Fanatics wants you.
We're hiring full-time Applied AI Engineers to join a small, senior team that sits at the center of how Fanatics transforms itself with AI. Our goal is simple and ambitious: make Fanatics a leaner, faster, more automated company. Each Applied AI Engineer works like a tiger team: embed with a business function, find its biggest bottlenecks, replace slow, manual work with AI agents and workflows that actually run in production, then move on to the next challenge.
This is a doer's role, not an advisory one. You'll own problems end to end: define them, design the solution, build it, prove it works, and drive adoption. You'll have real autonomy, executive visibility, and a direct line to leadership, with joint reporting to the CFO and Corporate AI Transformation Lead.
We're looking for someone with prior experience doing applied AI work at a startup or fast-growing company, who is used to moving quickly with limited resources and is ready to bring that pace to a global brand.
What You'll Do
- Embed within corporate functions across the business, find the workflows where AI can remove the most manual effort or unlock the most growth, fix them, then rotate to the next function.
- Design, build, and deploy production-grade agents and agentic workflows using frontier models (Claude, GPT, Gemini) and the modern agent stack: MCP servers, agent SDKs, orchestration tools, browser and computer use, and evals.
- Move fast: take a high-impact use case from concept to working solution in weeks, not quarters, and prove it with measurable results.
- Think in systems and bias toward solving the problem, not toward building for its own sake. Sometimes the right answer is a custom agent; sometimes it's configuring an existing tool.
- Partner with business leaders to be accountable for outcomes, and help them rethink how their teams operate in an AI-first way.
- Call balls and strikes: pressure-test whether a problem is worth automating, what "good" looks like, and how to measure ROI.
- Help set the bar for AI across the company, including tool evaluation, security and InfoSec coordination, governance, and best practices.
- Spread the practice: document and showcase wins, coach colleagues, and help build a company culture that automates by default.
What We're Looking For
- 3+ years of experience in applied AI or software engineering, including time at a startup or fast-growing company where you shipped AI-powered products or automations to real users.
- A track record of building and deploying custom agents or agentic workflows with frontier LLMs that solved real business problems, not just prototypes.
- Fluency with the modern AI stack: LLM APIs, MCP, agent frameworks (Claude Agent SDK, OpenAI Agents SDK, LangGraph, or similar), prompt engineering, RAG, and evaluation methods.
- Strong coding skills (Python preferred) and the judgment to pick the right tool for the job, including buying or configuring instead of building.
- Comfort operating independently in ambiguity, with a high bar for speed, ownership, and follow-through.
- A strategic mindset paired with a "get it done" attitude; you can tell the difference between a cool demo and a business outcome.
- Excellent written and verbal communication, with the ability to earn trust and drive change with senior business leaders.
- Bachelor's degree in Computer Science, AI/ML, Software Engineering, or a related field preferred; equivalent practical experience welcome.
- Bonus points if you're a fan of sports, collectibles, or fandom culture (because we're a little obsessed).
Why You'll Love It Here
- Real ownership of high-visibility problems, with exposure to every part of Fanatics and direct access to senior leadership.
- A chance to be part of a small, hand-picked team shaping how a global sports and collectibles company becomes AI-first.
- Backing for your work: dedicated headcount, budget, and executive sponsorship to get things done.
- Competitive salary and comprehensive medical, dental, and vision coverage.
- 401(k) plan with company match.
- Generous paid time off, paid holidays, and parental leave.
- Wellness benefits and a learning & development stipend.
- NYC office with daily in-person collaboration.
- Employee discount on Fanatics products and (yes) some sweet Fanatics swag.
Final Word
If you've built AI that moved a business and you're ready to do it at global scale, let's talk. This is where the doers build.
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Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fanatics, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Fanatics AI Hiring
Fanatics has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span New York, NY, US, Redwood City, CA, US, New York, NY. Compensation range: $200K - $225K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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