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About This Role
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 a Staff AI/ML Engineer, you will 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 practices, model evaluation, responsible AI deployment, and GenAI system architecture, operating as the highest individual contributor in the AI/ML engineering discipline and influencing technical direction at an organizational level. In this role, you will shape AI product strategy through deep technical expertise, develop the next generation of senior ML engineering leaders, and serve as a recognized authority both internally and in the broader AI/ML community. You will set the bar for technical innovation, responsible AI, and engineering excellence across multiple teams.
Select responsibilities include:
- 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, serving reliability at massive scale, and novel model quality problems with no established playbook
- Establish AI/ML engineering standards for experiment reproducibility, model evaluation rigor, responsible AI practices, and production reliability adopted across all teams
- Drive capability in frontier AI/ML techniques including large\-scale foundation model training, advanced fine\-tuning methods, and next\-generation GenAI system design
- Consult with applied science leaders and enterprise architects on AI system architecture decisionsprovidingexpert guidance on feasibility, trade\-offs, and long\-term engineering implications
- Lead technical design reviewsprovidingexpert guidance on ML system architecture, training strategy, and platform evolution
- Mentor senior and staff engineers across the domain developing AI/ML engineering leaders and building organizational capability
Qualifications
- Bachelor's orMaster's degree in Computer Science, Machine Learning, or related technical field, or equivalent experience; advanced degree (Master'sor PhD) preferred
- 9\-12 years of AI/ML engineering experience defining AI/ML engineering strategy andestablishingstandards across the organization, or equivalent, which includes educational experience (e.g., PhD degree)
- Demonstrated ability to set software engineering standards for complex AI/ML application development including design patterns, testing frameworks, and service architecture adopted across all AI/ML engineering teams
- Demonstrated ability to set strategy for design and implementation ofhighly complexmodels with multiple AI/ML components, training infrastructure, and evaluation rigor at enterprise scale
- Demonstrated ability to setMLOpsstrategy including CI/CD standards, serving platform architecture, and deployment practices
- Demonstrated ability to set GenAI engineering strategy including foundation model selection, fine\-tuning infrastructure, and responsible AI standards
- Track recordmentoring senior and staff engineers; define the mentorship and technical development strategy for the AI/ML engineering 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 achievegreat 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, akadoesn’ttake yourself too seriously.
additional notes
Please note: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of employment visa at this time 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 $211,880 \- $278,090 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.
\#LI\-AK1
Salary Context
This $211K-$278K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At lululemon, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($244K) sits 14% above the category median. Disclosed range: $211K to $278K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
lululemon AI Hiring
lululemon has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $193K - $350K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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