FanDuel is seeking a Director of Machine Learning Engineering to lead a team 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. The goal is to own the strategy and execution to deliver scalable, production-grade ML services that power key business decisions and customer experiences.
Requirements
- Proven experience building and managing robust, scalable machine learning pipelines and platforms.
- Expertise in modern ML and data technologies (e.g., Spark, Airflow, Kubeflow, Databricks, Kafka, TensorFlow/PyTorch, Feast, AWS ML services)
- Strong track record of partnering with data scientists to bring models into production and optimize performance
- Experience working with cloud platforms such as AWS, GCP, or Azure
- Understanding of machine learning workflows and advanced analytics pipelines
- Familiarity with ML product thinking and customer-centric data delivery
- 8+ years of experience in data science, machine learning or data engineering, with 3+ years in a technical leadership or management role with a focus on machine learning
Responsibilities
- 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
- 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 scientists, data product, engineering, and executive leadership to identify and prioritize ML use cases that deliver clear business value
- Improve development workflows to optimize productivity, observability, delivery speed, and quality
Other
- 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
- Serve as a bridge between data scientists, machine learning engineers, and platform engineers—ensuring models move seamlessly from prototype to production
- Excellent communication skills with the ability to influence technical and non-technical audiences
