Lululemon

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 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 at scale. The 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. 

Core responsibilities 

As an AI/ML Engineer, you will contribute to the design and implementation of AI/ML systems across the full model lifecycle from dataset curation, feature engineering, and model training through evaluation, deployment, and production monitoring. You will contribute to meaningful technical decisions on model architecture, training strategies, and serving infrastructure, working with minimal supervision to deliver reliable, high-quality AI capabilities aligned to business objectives. You will collaborate closely with research scientists, data scientists, and product engineers to translate model innovations into scalable production systems, and begin to develop mentorship and knowledge-sharing habits within the team. 

Select responsibilities include:

• Design and implement end-to-end AI/ML systems covering dataset curation, feature engineering, model training, hyperparameter optimization, evaluation, deployment, and retraining
• Apply appropriate model architectures, training strategies, and optimization techniques for given tasks including classification, regression, ranking, generation, and retrieval
• Build and maintain scalable model training pipelines using distributed training frameworks (PyTorch Distributed, Horovod, Ray Train) and cloud ML platforms (SageMaker, Vertex AI, Azure ML)
• Deploy models to production using CI/CD automation including A/B testing, canary rollouts, shadow deployment, and automated rollback procedures
• Contribute to implementation of model serving infrastructure meeting latency, throughput, and cost requirements using serving frameworks (TorchServe, Triton, BentoML, or equivalent)

Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Statistics, or related technical field, or equivalent experience; Master's degree in ML or AI beneficial
• 4-8 years of AI/ML engineering experience designing and owning end-to-end ML systems including model training, evaluation, and production deployment, or equivalent, which includes educational experience (e.g., Master's degree)
• Proven ability to design and implement production-grade AI/ML application components applying best-practice software engineering including CI/CD integration, automated testing, and service reliability patterns
• Demonstrated experience with independently design model training strategies including architecture selection, optimization approach, and evaluation methodology aligned to business requirements
• Demonstrated experience owning full model deployment lifecycle including CI/CD automation, canary releases, A/B testing, and automated rollback
• Demonstrated ability to define ML Ops platform standards and reusable deployment templates adopted across the domain
• Experience with common ML tools and frameworks and implementation such as Python, Spark, Airflow, MLFlow, feature stores, cloud ML platforms

Must haves

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

•    Possess an entrepreneurial spirit and continuously innovate to achieve great results. 

•    Communicate with honesty and kindness and create the space for others to do the same. 

•    Lead with courage, knowing the possibility of greatness is bigger than the fear of failure. 

•    Foster connection by putting people first and building trusting relationships. 

•    Integrate fun and joy as a way of being and working, aka doesn’t take yourself too seriously.