Director of Machine Learning Engineering - Careers
Listed on 2025-12-15
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Director of Machine Learning Engineering
ABOUT FANDUEL
Fan Duel Group is the premier mobile gaming company in the United States and Canada. Fan Duel Group consists of a portfolio of leading brands across mobile wagering including:
America’s #1 Sports book, Fan Duel Sports book; its leading iGaming platform, Fan Duel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, Fan Duel Racing; and its daily fantasy sports product.
In addition, Fan Duel Group operates Fan Duel TV, its broadly distributed linear cable television network and Fan Duel TV+, its leading direct-to-consumer OTT platform. Fan Duel Group has a presence across all 50 states, Canada, and Puerto Rico.
The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.
Fan Duel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).
THE POSITIONOur roster has an opening with your name on it
We are seeking a Director of Machine Learning Engineering to lead a world-class team at the intersection of data science and machine learning engineering. This role is responsible for scaling the development, deployment, and operationalization of advanced machine learning systems across the organization—including personalization, forecasting, optimization, generosity, search and customer segmentation models.
You will own the strategy and execution to deliver scalable, production-grade ML services that power key business decisions and customer experiences. This includes partnering closely with data science, engineering, and product teams to align on ML use cases, model performance, infrastructure needs, and long-term reusable architecture.
If you’re a visionary leader with deep technical expertise in both data science and ML engineering and are passionate about building reusable ML services that deliver measurable business outcomes at scale—this is the role for you.
In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.
THE GAME PLAN
Everyone on our team has a part to play
Strategic Leadership
- Define and execute the strategy for machine learning services across the company, aligning with business goals and customer needs
- Establish a vision and roadmap for scalable ML infrastructure, model lifecycle management, and ML-powered product capabilities
- Partner with data science, data product, engineering, and executive leadership to identify and prioritize ML use cases that deliver clear business value
- Hire, mentor, and grow a high-performing team of ML engineers
- Create a culture of innovation, experimentation, and accountability—fostering best practices in model development, software engineering, and reproducibility
- Provide technical and strategic mentorship to elevate the quality and impact of ML projects across the company
ML Engineering at Scale
- Lead the development and deployment of end-to-end ML systems—from experimentation and training to inference, monitoring, and continuous learning
- Guide the team in building reusable model components, APIs, and pipelines for personalization, forecasting, fraud detection, and more
- Ensure ML services are highly available, scalable, secure, and cost-efficient—leveraging modern MLOps practices and tooling
Cross-Functional Collaboration
- Serve as a bridge between data scientists, machine learning engineers, and platform engineers—ensuring models move seamlessly from prototype to production
- Standardize model governance, performance monitoring, and retraining workflows in collaboration with stakeholders across teams
- Translate complex ML capabilities into stakeholder-friendly language and value statements that resonate with business and product leaders
Operational Excellence
- Improve development…
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