AI/ML Engineer – Build Production-Grade Models & Pipelines

lululemon; Seattle (WA); On-site; USD 147,000 - 193,000; Full time

Benefits offered by this job

Extended health and dental benefits

Paid time off

Retirement plan matching

Employee discount

Fitness & yoga classes

Parenthood top-up

Development courses

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 collaborate with research scientists, data scientists, and product engineers to translate innovations into scalable production solutions.

The role emphasizes CI/CD, model deployment, and scalable serving infrastructure, with onsite collaboration expected at Seattle campuses. Strong Python/Spark, Airflow, MLFlow experience is preferred.

Qualifications

• Bachelor’s degree in CS/ML/Stats or related field, or equivalent experience.
• 4–8 years of AI/ML engineering experience designing and owning end-to-end ML systems.
• Production-grade AI/ML components with CI/CD, automated tests, and reliability patterns.
• Independent design of model training strategies aligned to business needs.
• Full model deployment lifecycle including CI/CD, canary releases, A/B testing, rollback.
• Ability to define ML Ops standards and reusable deployment templates.

Responsibilities

• Design end-to-end AI/ML systems from dataset curation to retraining.

Skills

AI/ML engineering

CI/CD

ML Ops standards

Model deployment

Python/Spark

Cloud ML platforms

ETL/feature engineering

Education

Bachelor’s degree in CS/ML/Stats or equivalent

Master’s in ML/AI beneficial

Tools

Python

Spark

Airflow

MLFlow

Feature stores

Cloud ML platforms

Job description

lululemon is seeking an AI/ML Engineer to design and implement end-to-end AI/ML systems across the full model lifecycle. You will collaborate with research scientists, data scientists, and product engineers to translate innovations into scalable production solutions.

The role emphasizes CI/CD, model deployment, and scalable serving infrastructure, with onsite collaboration expected at Seattle campuses. Strong Python/Spark, Airflow, MLFlow experience is preferred.