lululemon AI/ML Engineer

lululemon; Vancouver; On-site; CAD 127,000 - 167,000; Full time

Benefits offered by this job

Extended health and dental benefits

Paid time off

Savings and retirement plan matching

Generous employee discount

Fitness & yoga classes

Parenthood top-up

Development course offerings

Job summary

lululemon is seeking an AI/ML Engineer to design and implement end-to-end AI/ML systems across the full model lifecycle. You will work with data scientists, researchers, and product engineers to build scalable training pipelines and production-ready solutions that align with business goals.

This onsite role in Vancouver requires 4+ days per week in the office, strong ML engineering fundamentals, and experience with cloud ML platforms and CI/CD practices.

Qualifications

• Bachelor's degree in Computer Science, ML, Statistics, or related field; Master's beneficial.
• 4-8 years of AI/ML engineering experience with end-to-end ML systems and deployment.
• Experience with CI/CD integration, automated testing, and reliability patterns.
• Proven ability to design training strategies and production deployment lifecycle.
• Experience with ML Ops standards and reusable deployment templates.

Responsibilities

• Design and implement end-to-end AI/ML systems including dataset curation, feature engineering, training, evaluation, deployment, and retraining.
• Apply model architectures and optimization techniques for classification, regression, ranking, generation, and retrieval.
• Build scalable training pipelines using distributed frameworks and cloud ML platforms.
• Deploy models to production with CI/CD automation and A/B testing, canary releases, and rollback procedures.
• Develop model serving infrastructure meeting latency, throughput, and cost targets.

Skills

AI/ML engineering

Model deployment

CI/CD integration

Python

Spark

Education

Bachelor's degree in Computer Science, ML, or related field

Master's degree beneficial

Tools

PyTorch Distributed

Horovod

Ray Train

SageMaker

Vertex AI

Azure ML

TorchServe

Triton

BentoML

Airflow

MLFlow

Job description

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 teamperformance. Thetypical 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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