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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 Data \& AI Security team is responsible for protecting the organization’s most critical data and AI assets while enabling responsible, scalable innovation. We build and operate Data Security Posture Management (DSPM) and AI Security Posture Management (AISPM) capabilities across a complex, global ecosystem. Our work is highly cross\-functional, partnering with Data \& Analytics, Engineering, Legal, Privacy, and GRC teams to embed security by design while keeping the organization fast, compliant, and resilient. Our focus is on visibility, prevention, and remediation at scale, balancing strong security controls with minimum business friction.
The Senior Cybersecurity Engineer is responsible for securing the organization’s AI ecosystem by discovering, assessing, and mitigating risks associated with AI models, data usage, model lifecycle, and AI\-driven workflows. This role partners closely with Data Governance, Platform Engineering, and Security Architecture to enable safe and scalable AI adoption while ensuring regulatory, privacy, and corporate policy alignment.
Core responsibilities
As a Senior Cybersecurity Engineer, you will lead complex security engineering initiatives, designing and implementing security controls, platforms, and solutions that protect critical systems at scale. You will build and operate enterprise security capabilities such as centralized authentication, security enforcement mechanisms, and security automation, while applying secure coding practices and rigorous testing and validation. You will partner closely with engineering teams to embed security into system design and delivery, drive improvements to security quality and reliability, and mentor junior engineers through hands\-on technical leadership and knowledge sharing.
Select responsibilities include:
- Build enterprise security systems implementing centralized authentication, security platforms, and organization\-wide controls
- Lead comprehensive threat modeling and security assessments for complex systems, evaluating attacker behaviour across integrations and influencing secure design decisions early in the development lifecycle
- Own end\-to\-end response for complex security incidents, driving deep root cause analysis and delivering coordinated long\-term improvements to detection, prevention, and security monitoring capabilities
- Develop advanced security code, tools, and libraries including security automation platforms, scanners and detectors, security testing systems, and security SDKs
- Establish security code standards defining secure coding practices, code review requirements, and implementation quality
Qualifications
- Bachelor’s degree in Computer Science, Cybersecurity, or related field; security certification strongly preferred
- 6\-10 years of experience leading security engineering initiatives, establishing security standards and practices, and building security systems at scale, or equivalent.
- Experience with Data Security Posture Management (DSPM), Data Loss Prevention (DLP), AI Security Posture Management (AISPM), or related data security technologies, including data discovery, classification, run time monitoring, and policy enforcement across enterprise and cloud environments.
- Experience owning, operating, or maturing enterprise security platforms and capabilities, including operationalization, roadmap execution, adoption, and continuous improvement.
- Strong systems engineering mindset with experience designing scalable solutions, integrations, automation, and operational processes.
- Experience translating security findings into prioritized remediation plans and measurable risk reduction outcomes.
- Experience identifying and mitigating risks associated with sensitive data exposure, AI\-enabled solutions, cloud data platforms, and emerging technologies.
- Proven ability to lead enterprise\-scale security capabilities and initiatives, driving alignment, adoption, and measurable outcomes across technical and business teams
- Experience defining metrics, reporting, operational processes, and service models that support sustainable security capabilities at enterprise scale.
- Strong communication and collaboration skills, with the ability to explain complex technical concepts and influence decisions across engineering, cybersecurity, data, privacy, legal, and business teams.
- Experience mentoring engineers and contributing to technical direction, best practices, and capability maturity within a security engineering organization.
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.
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 an 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 $147,300 \- $193,300 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, 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
This role is classified as In\-Person under our SSC Workplace Policy:
In\-person collaboration and/or office\-based work is necessary or important for the role. Work is mainly performed onsite, 4\-5 days per week depending on role requirements.
Salary Context
This $147K-$193K range is below the median 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 in Demand for This Role
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 ($170K) sits 21% below the category median. Disclosed range: $147K to $193K.
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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