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About This Role
Job ID: 291793
Location Name: CA\-FSC SF Off (0174\)
Address: 350 Mission St, 20th Floor, San Francisco, CA 94105, United States (US)
Job Type: Full Time
Position Type: Regular
Job Function: Information Technology
Work Location: Hybrid\-San Francisco Office
Belong to Something Beautiful
At Sephora, beauty is about feeling seen, valued, and empowered, individually and collectively. It is connecting deeply with others, celebrating diversity and inclusivity, unlocking your potential, and making a difference every day. Together, we belong to something beautiful.
Your Role at Sephora:
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Ready for a career glow up? As Senior Director of Applied AI and Data Solutions , you'll be establishing and leading the Applied AI \& Data Solutions vertical within Business Systems. This is a rare opportunity to build from the ground defining strategy, assembling a team, and driving transformative Agentic AI adoption, Business Automation across Supply Chain, Merchandising, and Finance domains. This leader will help translate cutting\-edge AI capabilities into tangible business outcomes, working directly with business leaders to identify high\-impact opportunities and deliver production\-ready solutions that drive measurable value. This role requires equal parts strategic vision and hands\-on execution expertise.
What You'll Do:
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- Build AI Business Cases. Develop ROI\-driven business cases for Agentic AI initiatives, partnering with domain leaders to identify high\-impact opportunities and translate them into scalable AI/data solutions — including digital agents, productivity automation, and AI\-assisted decision\-making.
- Drive Executive Stakeholder Relationships. Manage C\-level and cross\-functional relationships across Supply Chain, Merchandising, and Finance, grooming AI ideas into strategic initiatives, innovation programs, and actionable roadmaps aligned with enterprise objectives.
- Lead and Scale a High\-Performing Team. Build, mentor, and grow a team of data scientists, ML engineers, and analytics professionals — fostering a culture of innovation, experimentation, and continuous learning while developing talent pipelines across the organization.
- Define Agentic AI and Automation Strategy. Set the vision and execution strategy for Agentic AI and Business Automation aligned with enterprise goals, identifying and prioritizing AI/ML opportunities across key retail domains.
- Partner with Technology Leadership. Collaborate directly with VP/CTO/CIO on AI enablement and foundational technology setup, influencing all levels of the organization to embrace AI\-first transformation and new ways of operating.
What You'll Bring:
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- 10\+ years in AI/ML and data science leadership , including Director\-level or above experience delivering production AI/ML solutions at scale in enterprise environments.
- Deep technical expertise in modern AI/ML techniques — including generative AI (LLMs, RAG), NLP, predictive modeling, computer vision, supervised/unsupervised learning, and data engineering platforms such as Databricks and Snowflake.
- Proven domain knowledge in retail operations — specifically Supply Chain, Merchandising, or Finance — with a demonstrated track record of driving organizational change and AI adoption at a strategic leadership level.
- Strong communication and executive influence skills , with the ability to build AI fluency across an organization, present to C\-suite stakeholders, and balance long\-term vision with short\-term execution.
- Bachelor of Science in Software Engineering or MBA from a prestigious institution required; AI/ML certifications preferred.
What You’ll Get:
The annual base salary range for this position is $271,530\.00 \- $301,700\.00 The actual base salary offered depends on a variety of factors, which may include, as applicable, the applicant’s qualifications for the position; years of relevant experience; specific and unique skills; level of education attained; certifications or other professional licenses held; other legitimate, non\-discriminatory business factors specific to the position; and the geographic location in which the applicant lives and/or from which they will perform the job. Individuals employed in this position may also be eligible to earn bonuses. This job will be posted for a minimum of five days.
- Caring Community. You’ll collaborate with teammates who are equally passionate about innovating and driving the industry forward – together, united in beauty.
- Fulfilling Path. Your career transformation starts here, with opportunities that will challenge, stretch and develop your skills.
- Meaningful Work. As you make an impact on beauty, you’ll feel and see the positive change (consumer, industry, and social) that your individual voice is a part of.
Rewards as Unique as You:
*Some benefits have eligibility requirements and may depend on job classification and length of employment.*
- Health . Choose a healthcare plan to fit you and your family’s needs with medical, dental, and vision coverage. Sephora also fully covers our employees’ disability and life insurance.
- Wealth . We offer a competitive 401k with 4% match as well as FSA and HSA programs. We also offer a Student Debt Retirement plan, where your student loan payments qualify to earn the 401k match from Sephora.
- Balance . You’ll be empowered to find the perfect blend of work/life balance that actually works for you with PTO, flexibility, protected leave, and more.
- Growth . Career growth is built into every role, with access to training, development, and tuition reimbursement.
- Perks . Think you’ve tried it all? Enjoy a 30% discount on all merchandise/services, opportunities for free product or “gratis,” and flash sale discounts on LVMH brand products.
- Support . Join a team that truly cares – with free mental health and financial coaching resources with 24/7 access to Modern Health and Financial Finesse. Plus, volunteer and donation matching.
Sephora values a diverse and inclusive workplace and considers all applicants without regard to sex, pregnancy, race, color, national origin, gender (including gender identity and gender expression), age, religion, sexual orientation, military/veteran status, disability, or any other protected category. Sephora is committed to providing reasonable accommodation ta applicants with disabilities or other medical conditions.
Sephora will consider all qualified applicants, including those with arrest and conviction records in a manner consistent with the requirements of all applicable laws, including the Los Angeles Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.
Join Us and Belong to Something Beautiful
Salary Context
This $271K-$301K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sephora, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($286K) sits 31% above the category median. Disclosed range: $271K to $301K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sephora AI Hiring
Sephora has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $301K - $301K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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