Interested in this AI/ML Engineer role at L'Oréal?
Apply Now →Skills & Technologies
About This Role
Director, AI Digital Product Management, Augmented Beauty Tech Incubator, L'Oréal Research \& Innovation \- San Francisco, California
Hello, we’re L’Oréal, we're not just building brands; we're shaping how the world experiences beauty (and it takes a lot of cool jobs to do it).
Intrigued? Keep reading, this might be the opportunity you've been searching for.
Who We Are
Join us at L’Oréal, the world's \#1 beauty company present in over 150 markets. For over a century, we have been transforming; fueled by data, tech, innovation, and science. Together, we tackle big challenges while making sure we stay committed to making the world a more inclusive and a better place for everyone \& our planet.
At L’Oréal Research \& Innovation we are pushing the boundaries of Science \& Tech. We invest heavily in cutting\-edge research, leveraging advanced technologies to understand skin, hair, and microbiome, while discovering new active ingredients and launching outperforming formulas. Our 3,500 L'Oréal R\&I experts leverage Science \& Technology to invent safe, trustable, sustainable \& responsible beauty products and experiences that will change people’s lives.
Augmented Beauty Innovation is the startup\-style upstream arm of L’Oréal R\&I’s Beauty Tech organization. This team explores new technologies and ideas, then develops them in partnership with both external tech companies and internal beauty experts, turning them into functional prototypes with user\-centric designs and experiences that can be launched as beauty products and services.
Within Augmented Beauty Innovation, the Global Digital team leverages expertise in AI, digital product development, data science, and consumer UX to explore, prototype, and launch new digital services. This team is based in the Augmented Beauty US Hub but has a global remit within the digital space, collaborating closely with the Global CDMO, IT, and engineering teams to build new digital tech powered by R\&I and industry innovations.
The Director, Digital Product Management will lead AI and digital innovation efforts within this team, focusing on driving the success of specific products and ensuring they deliver measurable customer value and business results. This role encompasses the entire innovation lifecycle, blending strategic vision with tactical execution. They will identify, develop, and pilot groundbreaking digital products and services that leverage emerging technologies, with a focus on generative AI. Responsibilities will include concepting, exploring consumer needs, securing internal buy\-in through pitches, building and leading cross\-functional project teams, working within the L’Oreal Innovation process, and guiding projects through prototyping and handoff to development teams for implementation. They will input into the strategic roadmap that defines the future of digital beauty within the organization, ensuring innovations are both aspirational and achievable.
A Day in the Life: As a Director of Digital Product Management you will:
- AI Services Innovation: be a thought leader in how AI\-powered services integrate into and reinvent e\-commerce platforms, digital infrastructure, professional tools, and in\-store experiences. Lead the hands\-on development of new digital products, including prompt engineering, behavior optimization, and rapid prototyping.
- Product Execution: drive the product innovation lifecycle from conception to pilot launch, serving as the bridge between technical innovation, L’Oreal Research Scientists, and business objectives of the Groupe’s brands and divisions. Translate complex challenges into actionable development sprints.
- User Value Proposition champion the strategic analysis, positioning, and product narratives to select product features that resonate deeply with our target users, ensuring successful market adoption.
- Project Leadership: lead product requirements and definition; collaborate with cross\-functional internal teams, including IT, operations, marketing, and portfolio management; and guide projects through the L’Oréal process.
We Are Looking For: You’re a great match for this role if you are an innovator, entrepreneur, integrator, and problem\-solver with a research\-minded approach, eager to contribute to our Beauty Tech portfolio
- Education: A completed Bachelor's or Master’s in a scientific, technical or related field is required. Advanced degree preferred.
- Experience: 8\+ years of product management experience with a focus on digital innovation, including defining, launching, and optimizing digital products and features. Demonstrated success in end\-to\-end product ownership, from high\-level strategy to detailed execution.
- 3\+ years of experience leading strategic development and execution of digital products from concept through prototyping, iteration, and handoff to development teams, with experience using lean startup and agile methodologies.
- Strong expertise in all stages of the digital product lifecycle, including user research, prototyping, iterative development, and agile methodologies.
- Applied AI experience launching AI/ML\-driven products; proficiency in prompt design techniques, agentic workflows, context window management, and system evaluation.
- Solid understanding of digital interface design and user experience principles, with the ability to translate consumer insights into engaging and intuitive digital product experiences.
- Deep knowledge of how augmented reality, generative AI, big data, and other digital beauty applications integrate into broader digital ecosystems, including e\-commerce platforms, IT backend systems, and digital infrastructure.
- Proven ability to build, lead, and motivate cross\-functional digital product teams, including designers, developers, data scientists, and QA, to deliver innovative solutions.
- Exceptional communication and storytelling skills to articulate the vision, value, and strategy of digital beauty products to diverse audiences, from technical teams to marketing and sales stakeholders.
- Experience managing day\-to\-day technical and design direction, launching successful app products in the market, and developing products with an understanding of cutting\-edge digital innovation technologies
- Experience with complex product/outbound customer work; knowledge of market, landscape, and customer/user requirements in
- Strong ability to influence stakeholders and build consensus for product vision and strategy through effective communication, internal pitching, and collaboration.
- The ability to be onsite, in the office 3 days a week as this is an essential function of the position (required)
- Authorization to work in the United States on a full\-time, permanent, ongoing basis is required. No immigration support/sponsorship offered for this role.
What’s In It For You
- A place for you to leave your comfort zone and grow beyond your potential (here, you’ll be encouraged to try new things and take risks!)
- Real responsibility from day 1, there’s no sitting on the sidelines at L’Oréal
- An environment where people of every ethnicity, social background, age, religion, gender and sexual orientation as well as people with disabilities are accepted, can speak up, will thrive and are celebrated!
- A place where you can contribute to something bigger! Many of our brands have societal /environmental causes to make concrete difference
- Base Salary Range: $138,500 \- $200,800 (The actual compensation will depend on a variety of job\-related factors which may include geographic location, work experience, education, and skill level)
- Competitive Benefits Package (Medical, Dental, Vision, 401K, Pension Plan)
- Hybrid Work Policy (Up to 2 Days per week work from home for eligible roles, subject to manager approval)
- Flexible Time Off (Paid Company Holidays, Paid Vacation, Vacation Buy Program, Volunteer Time, Summer Fridays \& More!)
- Access to Company Perks (VIP Access to L’Oréal’s Internal Shop for Discounted Products, Monthly Mobile Allowance)
- Learning and Development Opportunities (Unlimited Access to E\-learnings, Lunch \& Learn Sessions, Mentorship Programs, \& More!)
- Employee Resource Groups (Think Tanks and Innovation Squads)
- Access to Mental Health \& Wellness Programs
Don’t meet every single requirement? At L'Oréal, we are dedicated to building a diverse, inclusive, and innovative workplace. If you’re excited about this role but your past experience doesn’t align perfectly with the qualifications listed in the job description, we encourage you to apply anyways! You may just be the right candidate for this or other roles!
We are an Equal Opportunity Employer and take pride in a diverse environment. We’re committed to guaranteeing inclusive recruitment processes and advocating for hiring and promoting each candidate in an ethical and equitable way. The Group strictly prohibits discrimination against any applicant for employment because of the individual’s gender identity or expression, sexual orientation, visible and/or invisible disabilities, socio\-economic and/or multicultural origins, health conditions, age, religion, or any other characteristics protected by law.
Salary Context
This $138K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1889 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 3,736 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At L'Oréal, 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 $181,357 based on 12,694 positions with disclosed compensation. Director-level AI roles across all categories have a median of $248,100. This role's midpoint ($169K) sits 6% below the category median. Disclosed range: $138K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,650. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $248,100; VP: $250,000.
L'Oréal AI Hiring
L'Oréal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $200K - $200K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,736 open positions tracked in our dataset. By seniority: 109 entry-level, 1,755 mid-level, 1,486 senior, and 386 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (562 positions). The remaining 3,158 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,650. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,736 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,564), Data Scientist (311), AI Software Engineer (277). 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 (109) are outnumbered by mid-level (1,755) and senior (1,486) 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 386 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (562 positions), with 3,158 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 $200,100. Top-quartile roles start at $253,650, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,942 postings), Aws (1,175 postings), Azure (881 postings), Rag (827 postings), Gcp (718 postings), Prompt Engineering (590 postings), Pytorch (586 postings), Claude (528 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.