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lululemon

AI/ML Engineer

Vancouver, BC, Canada

Job type: Full-time
Apply by: Oct 27, 2026
Posting language: English
Posted: Oct 1, 2026

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Original job description

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 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.

Additional Notes

Authorization to work in Canada is required for this role

Compensation And Benefits Package

lululemon’s compensation offerings are grounded in a pay-for-performance philosophy that recognizes exceptional individual and team performance. The typical hiring range for this position is from $127,140-$166,870 annually ; the base pay offered is based on market location and may vary depending on job-related knowledge, skills, experience, and internal equity. As part of our total rewards offering, permanent employees in this position may be eligible for our competitive annual bonus program and equity offerings, subject to program eligibility requirements.  

At Lululemon, Investing In Our People Is a Top Priority. We Believe That When Life Works, Work Works. We Strive To Be The Place Where Inclusive Leaders Come To Develop And Enable All To Be Well. Recognizing Our Teams For Their Performance And Dedication, Other Components Of Our Total Rewards Offerings Include Support Of Career Development, Wellbeing, And Personal Growth

Extended health and dental benefits, and mental health plans
Paid time off
Savings and retirement plan matching
Generous employee discount
Fitness & yoga classes
Parenthood top-up
Extensive catalog of development course offerings
People networks, mentorship programs, and leadership series (to name a few)

Note: The incentive programs, benefits, and perks have certain eligibility requirements. The Company reserves the right to alter these incentive programs, benefits, and perks in whole or in part at any time without advance notice.

workplace arrangement

In-person collaboration and connection is important to our culture. Work is performed onsite, minimum 4 days per week.

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More about this job

Responsibilities

Design and implement end-to-end machine learning systems, including data preparation, feature engineering, training, evaluation, deployment, and production monitoring. Build scalable training and serving infrastructure, apply deployment practices such as A/B testing and canary releases, and collaborate with research, data science, and product engineering teams.

Requirements

A bachelor's degree in computer science, machine learning, statistics, or a related field, or equivalent experience, is required; a master's degree in machine learning or AI is beneficial. The role calls for 4–8 years of AI/ML engineering experience, production ML system ownership, strong software engineering and MLOps practices, and experience with Python, ML frameworks, cloud platforms, and deployment automation.

Skills

Python
Spark
Airflow
MLflow
PyTorch Distributed
Horovod
Ray Train
Amazon SageMaker
Vertex AI
Azure Machine Learning
TorchServe
NVIDIA Triton
BentoML
Feature Engineering
Model Training
MLOps

Benefits

Annual Bonus
Equity Offerings
Extended Health Benefits
Dental Benefits
Mental Health Plans
Paid Time Off
Savings and Retirement Plan Matching
Employee Discount
Fitness and Yoga Classes
Parenthood Top-Up
Development Courses
People Networks
Mentorship Programs
Leadership Series

Visa sponsorship

Not detected in the job text

Keywords

Artificial Intelligence
Machine Learning
Python
Spark
Airflow
MLflow
Feature Stores
PyTorch Distributed
Horovod
Ray Train
Amazon SageMaker
Vertex AI
Azure Machine Learning
TorchServe
NVIDIA Triton
BentoML
Distributed Training
Feature Engineering
Hyperparameter Optimization
Model Evaluation
Model Deployment
Model Serving
MLOps
CI/CD
A/B Testing
Canary Releases
Shadow Deployment
Automated Rollback
Model Monitoring
Responsible AI

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