Staff AI/ML Engineer: GenAI & Enterprise Scale (Onsite 4 days)

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

Benefits offered by this job

Health benefits

Paid time off

Retirement plan matching

Employee discount

Wellbeing programs

Career development

Mentorship programs

Job summary

lululemon is seeking a Staff AI/ML Engineer to define the technology AI/ML engineering approach and solve complex model development, training infrastructure, and AI system reliability challenges.

You will set domain standards for ML experimentation, model evaluation, responsible AI deployment, and GenAI system architecture as the highest contributor, shaping AI product strategy and mentoring senior engineers across teams.

Qualifications

• Bachelor's or Master's in CS, ML, or related field; advanced degree preferred.
• 9–12 years of AI/ML engineering experience defining strategy and standards across an organization.

Responsibilities

• Define the technical vision and strategy for AI/ML engineering platform spanning model development, training infrastructure, serving, evaluation, and governance.
• Solve complex AI/ML engineering challenges including large-scale training instability and serving at massive scale.
• Establish AI/ML engineering standards for experiment reproducibility, model evaluation rigor, responsible AI practices, and production reliability.
• Drive capability in frontier AI/ML techniques including foundation model training and GenAI system design.
• Consult with enterprise architects on AI system architecture decisions providing guidance on feasibility and trade-offs.
• Lead technical design reviews with ML system architecture and platform evolution guidance.
• Mentor senior and staff engineers to develop AI/ML leadership.

Skills

AI/ML strategy

MLOps

Mentoring

Python

Communication

Education

Bachelor's/Master's in CS or ML

Tools

Python

Spark

Airflow

MLFlow

Feature stores

Cloud ML platforms

Job description

lululemon is seeking a Staff AI/ML Engineer to define the technology AI/ML engineering approach and solve complex model development, training infrastructure, and AI system reliability challenges.

You will set domain standards for ML experimentation, model evaluation, responsible AI deployment, and GenAI system architecture as the highest contributor, shaping AI product strategy and mentoring senior engineers across teams.