Principal AI/ML Engineer

Requirements

Description & Requirements

Who we are

lululemon is an innovative performance apparel company for yoga, running, training, and other athletic pursuits. Setting the bar in technical fabrics and functional design, we create transformational products and experiences that support people in moving, growing, connecting, and being well. We owe our success to our innovative product, emphasis on stores, commitment to our people, and the incredible connections we make in every community we're in. As a company, we focus on creating positive change to build a healthier, thriving future. In particular, that includes creating an equitable, inclusive and growth-focused environment for our people.

About This Team

The Enterprise Data & AI organization is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and services that enable the enterprise to operate with intelligence at scale. The team leads the design and delivery of a trusted unified data foundation, AI-driven data analytics and insights, and AI solutions across lululemon’s vertically integrated retail ecosystem, while embedding strong data governance and responsible AI practices from the very beginning. By applying AI to critical business challenges and creating new, transformative AI solutions, the team helps reshape how lululemon operates. Through deep partnership with product, technology, and business teams, Enterprise Data & AI accelerates product innovation, unlocks measurable value, elevates guest and educator experiences, and drives enterprise efficiency.

Core responsibilities

As a Principal AI/ML Engineer, you will define and drive implementation approaches for enterprise-scale AI initiatives that transform how lululemon operates and delivers value. You will leverage deep expertise in machine learning systems, generative AI, MLOps , and platform architecture to shape company-wide technical strategy and design of the enterprise AI platform and services, and AI solutions.

You will collaborate closely with enterprise architects, applied science leaders, product leaders, and engineering organizations to translate the AI strategy and priorities into scalable technical solutions. Acting as a trusted advisor across the enterprise, you will guide critical decisions related to the architecture for enterprise AI platform and services, the evolution of the AI platform, and responsible AI implementation.

You will operate as a hands-on technical leader, advisor, and thought leader, influencing organizational direction through your technical expertise, and as an individual contributor, guide the build of AI/ML engineering capabilities across the company.This role may require availability outside of standard business hours, including on-call support, based on business needs.

Select Responsibilities Include

• Define the enterprise AI/ML engineering vision and implementation strategy spanning the enterprise AI platform and services, and common frameworks and tools for model development, training infrastructure, inference platforms, evaluation frameworks, and governance capabilities.
• Drive implementation of major AI initiatives, ensuring scalable, reliable, secure, and cost-effective AI solutions across the enterprise.
• Demonstrate expert-level coding proficiency by developing critical components of AI platforms, ML infrastructure, and production AI systems when hands-on technical leadership is required.
• Partner with enterprise architects, product team, and the Data and AI team to shape company-wide AI architecture, providing deep technical expertise on implementation feasibility, operational considerations, scalability, and long-term maintainability.
• Lead technical design of foundation model services, generative AI solutions, and agentic frameworks.
• Challenge and improve existing AI architectures by identifying platform limitations, technical risks, and opportunities for innovation, proposing solutions that advance enterprise AI capabilities.
• Provide executive-level technical advisory on complex AI investments, build-versus-buy decisions, model strategy, technology selection, responsible AI considerations, and implementation risk assessment.
• Drive enterprise-wide AI/ML engineering practices including model lifecycle management, experimentation standards, evaluation methodologies, observability, reliability, and responsible AI controls.
• Influence product strategy and business investment decisions through technical insights on AI feasibility, value realization, scalability, and organizational impact.
• Drive engineering productivity and AI adoption across multiple teams through platform enhancements, reusable AI capabilities, automation, and reduction of systemic technical barriers.
• Mentor Senior and Staff eng