Senior AI/ML Platform Engineer – GenAI & MLOps

lululemon; Seattle (WA); On-site; USD 212,000 - 278,000; Full time

Benefits offered by this job

Extended health and dental benefits

Paid time off

Retirement plan matching

Employee discount

Job summary

lululemon’s Enterprise Data & AI team drives data platforms and AI solutions at scale, enabling the business to operate intelligently. The senior role defines the AI/ML engineering platform vision, aligns with product and technology, and grows engineering leadership across the organization.

The role partners with enterprise architects, shapes strategy, and leads capability development for responsible AI and production-grade ML systems.

Qualifications

• Lead AI/ML engineering strategy and standards across the organization.
• Set design patterns, testing frameworks, and service architectures for complex AI/ML apps.
• Guide development of GenAI systems, foundation models, and MLOps.
• Mentor senior staff and build organizational capability.
• Collaborate with product, tech, and business teams.

Responsibilities

• Define the technical vision for AI/ML platform spanning model dev, training infra, serving, governance.
• Address large-scale training instability and serving reliability at massive scale.
• Establish standards for experiment reproducibility and model evaluation.
• Advise on architecture decisions and long-term engineering implications.
• Lead design reviews and mentor engineers across the domain.

Skills

AI/ML leadership

Strategy development

Mentoring engineers

Stakeholder collaboration

Communication

Education

Bachelor's or Master's in CS/ML or related

PhD preferred

Tools

Python

Spark

Airflow

MLFlow

Feature stores

Cloud ML platforms

Job description

lululemon’s Enterprise Data & AI team drives data platforms and AI solutions at scale, enabling the business to operate intelligently. The senior role defines the AI/ML engineering platform vision, aligns with product and technology, and grows engineering leadership across the organization.

The role partners with enterprise architects, shapes strategy, and leads capability development for responsible AI and production-grade ML systems.