Applied AI Engineer at Fanatics in New York, NY

  • Company: Fanatics
  • Location: New York, NY, United States
  • Salary: $150K – $200K
  • Job type: full time
  • Workplace: onsite
  • Posted: 2026-10-19

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Job description

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 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

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