AI​/ML Engineer

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.

The Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and products that enable the enterprise to operate with intelligence  team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon's vertically integrated retail ecosystem, 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.

As a Staff AI/ML Engineer, you will define the Technology AI/ML engineering approach and solve complex model development, training infrastructure, and AI system reliability challenges. You will set domain standards for ML experimentation practices, model evaluation, responsible AI deployment, and GenAI system architecture, operating as the highest individual contributor in the AI/ML engineering discipline and influencing technical direction at an organizational level.

In this role, you will shape AI product strategy through deep technical expertise, develop the next generation of senior ML engineering leaders, and serve as a recognized authority both internally and in the broader AI/ML community. You will set the bar for technical innovation, responsible AI, and engineering excellence across multiple teams.

• Define the technical vision and strategy for AI/ML engineering platform spanning model development, training infrastructure, serving, evaluation, and governance
• Solve complex AI/ML engineering challenges including large-scale training instability, serving reliability at massive scale, and novel model quality problems with no established playbook
• Establish AI/ML engineering standards for experiment reproducibility, model evaluation rigor, responsible AI practices, and production reliability adopted across all teams
• Drive capability in frontier AI/ML techniques including large-scale foundation model training, advanced fine-tuning methods, and next-generation GenAI system design
• Consult with applied science leaders and enterprise architects on AI system architecture decisionsprovidingexpert guidance on feasibility, trade-offs, and long-term engineering implications
• Lead technical design reviewsprovidingexpert guidance on ML system architecture, training strategy, and platform evolution
• Mentor senior and staff engineers across the domain developing AI/ML engineering leaders and building organizational capability

• Bachelor's or Master's degree in Computer Science , Machine Learning, or related technical field, or equivalent experience; advanced degree ( Master's or PhD) preferred
• 9-12 years of AI/ML engineering experience defining AI/ML engineering strategy andestablishingstandards across the organization, or equivalent, which includes educational experience (e.g., PhD degree)
• Demonstrated ability to set software engineering standards for complex AI/ML application development including design patterns, testing frameworks, and service architecture adopted across all AI/ML engineering teams
• Demonstrated ability to set strategy for design and implementation ofhighly complex models with multiple AI/ML components, training infrastructure, and evaluation rigor at enterprise scale
• Demonstrated ability to set MLOps strategy including CI/CD standards, serving platform architecture, and deployment practices
• Demonstrated ability to set GenAI engineering strategy including foundation model selection, fine-tuning infrastructure, and responsible AI standards
• Track record mentoring senior and staff engineers; define the mentorship and technical development strategy for the AI/ML engineering domain
• Experience with common ML tools and frameworks and implementation such as Python, Spark, Airflow, MLFlow , feature stores, cloud ML platforms

• Acknowledge the presence of choice in every moment and take personal responsibility for your life.
• Possess an…