Domino's is hiring a Director of Machine Learning & Artificial Intelligence

The Director of Machine Learning & Artificial Intelligence (ML & AI) leads the enterprise’s ML & AI Development and Engineering Center of Excellence (COE), serving as the central force behind our AI strategy, execution, and innovation. This role is accountable for building and scaling the COE into a world-class capability hub that delivers production-grade AI/ML solutions across the business. As the senior-most leader of the ML & AI COE, this individual will define the strategic roadmap, architect the technical foundation, and cultivate the talent and culture necessary to accelerate enterprise-wide AI adoption. They will oversee the development of intelligent systems—from traditional ML models to cutting-edge generative AI agents—ensuring solutions are scalable, sustainable, and aligned with business priorities. This role requires a rare blend of visionary leadership and deep technical fluency. The ideal candidate is a builder and operator, equally comfortable setting bold direction and rolling up their sleeves to ensure delivery excellence. Key Responsibilities COE Leadership & Strategy Lead the ML & AI Center of Excellence as the enterprise’s central engine for AI innovation, engineering, and enablement. Define and evolve the enterprise-wide ML & AI strategy in alignment with business goals and emerging technology trends. Serve as the organization’s primary evangelist for responsible AI, driving awareness, education, and adoption across functions. Identify, prioritize, and champion high-impact AI opportunities that unlock business value and operational efficiency. Create resource plans, and track spend to budgets. Team & Capability Building Build and scale a high-performing ML & AI engineering organization, including hiring, mentoring, and org design. Foster a culture of innovation, experimentation, and continuous learning within the COE and beyond. Establish and enforce best practices for ML Ops, model lifecycle management, and platform scalability. Model Enablement & Productionization Empower data scientists by transforming models of all maturity levels—from exploratory notebooks to advanced prototypes—into robust, governed, and scalable production assets. Establish seamless handoff processes and shared tooling that allow data scientists to focus on experimentation and insight generation, while ML engineers ensure operational excellence, compliance, and long-term maintainability. Position the ML engineering function as a trusted partner and accelerator—removing friction, reducing time-to-value, and enabling faster iteration cycles through automation, observability, and reusable infrastructure. GenAI & Agentic Systems Innovation Collaborate closely with the enterprise GenAI enablement product owner to co-develop tailored agentic solutions that meet business needs and align with enterprise architecture and governance standards. Lead the development and integration of advanced generative AI capabilities, including tailored solutions. Working closely with consumers, and the Data engineering, quality and governance teams. Drive experimentation and rapid prototyping of intelligent agents that augment decision-making, automate workflows, and unlock new business capabilities. But prioritize and promote use cases that can drive real incremental value. Stay at the forefront of the GenAI ecosystem—evaluating open-source and proprietary models (e.g., LLaMA, Phi) and integrating them into scalable, secure, and responsible enterprise solutions. Technical Execution & Engineering Excellence Oversee the design, development, and deployment of custom AI agents, ML pipelines, and intelligent systems. Ensure seamless productionization of models with a focus on performance, reliability, and maintainability. This is primarily accomplished in python, and deployed as containers or onto databricks. Champion modern engineering practices such as containerization, CI/CD, and cloud-native infrastructure. Cross-Functional Collaboration Partner with Data Engineering, Data Science, and Solution Architecture COEs to ensure alignment and interoperability. Collaborate with business stakeholders to translate complex needs into scalable, value-driven AI solutions. Represent the ML & AI COE in enterprise governance, architecture, and innovation forums.