FanDuel is seeking a Director of Machine Learning Engineering to lead a world-class team at the intersection of data science and machine learning engineering, responsible for scaling the development, deployment, and operationalization of advanced machine learning systems across the organization.
Requirements
- 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
- 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
- Familiarity with ML product thinking and customer-centric data delivery
- Understanding of machine learning workflows and advanced analytics pipelines
Responsibilities
- 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
- 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
- Improve development workflows to optimize productivity, observability, delivery speed, and quality
- Drive efficiency and performance through effective resource planning and prioritization
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
- Translate complex ML capabilities into stakeholder-friendly language and value statements that resonate with business and product leaders
