Role Description Ready for a career glow up? As a Lead Machine Learning Engineer, you'll be the driving force behind the architecture, engineering, and deployment of cutting-edge AI/ML systems at enterprise scale. The work you do will impact beauty, as you redefine how we inspire and connect with our customers — building the next generation of intelligent, AI-powered experiences across the beauty space. You'll lead a team that's united in beauty, supported by those who are equally passionate about pushing the boundaries of applied AI, engineering excellence, and real-world product impact. What You'll Do - Architect & Engineer Production-Grade AI/ML Systems: Design, build, and maintain scalable ML and Agentic AI systems using established engineering design patterns. Lead security-first and reliability-first practices, maintain deep domain expertise in ML systems and LLM infrastructure, and proactively anticipate future technical needs, scalability requirements, and cost implications. (20%) - Own End-to-End ML Solutions: Engineer and own batch and real-time model serving, agentic pipelines, RAG systems, and LLMOps infrastructure. Build and maintain robust tooling for monitoring, observability, logging, automated testing, performance testing, and A/B experimentation to ensure production reliability and continuous improvement. (20%) - Establish & Optimize ML Pipelines: Build scalable, efficient, and automated pipelines for data processing, feature engineering, model development, validation, evaluation, and deployment — ensuring reproducibility, quality, and operational excellence across the full ML lifecycle. (15%) - Deliver High-Quality Code in a Continuous-Release Environment: Write clean, efficient, and well-structured code to deliver AI/ML products iteratively. Uphold high engineering standards including code reviews, CI/CD integration, and test coverage across ML services and agentic workflows. (15%) - Partner Cross-Functionally to Shape AI/ML Capabilities: Collaborate closely with Product, Engineering, Data Scientists, ML Engineers, and Business stakeholders to define, scope, and plan new AI/ML capabilities — translating business requirements into technically sound, scalable engineering solutions. (10%) - Drive Delivery Planning & Engineering ROI: Review and prioritize epics and projects with clear breakdown, dependency management, and delivery planning. Proactively identify, communicate, and resolve blockers or delays. Navigate ambiguity and high-pressure situations with decisiveness, applying economic thinking to maximize value delivery. (10%) - Mentor, Grow & Inspire the Team: Mentor and develop ML Engineers and Data Scientists by promoting best practices in ML engineering, code quality, and operational excellence. Foster a culture of effective communication, continuous feedback, and knowledge sharing. Build strong cross-functional relationships and actively contribute to engineering strategy and the AI/ML product roadmap. (10%) Qualifications - Deep ML Engineering Expertise: 5+ years hands-on experience in model development, training pipelines, feature stores, model serving, and MLOps/LLMOps — with a proven ability to take systems from experimentation to production at scale. - Strong Software Engineering Fundamentals: 8+ years proficiency in Python, distributed systems, API design, and cloud-native architectures, with a strong command of engineering best practices including CI/CD, testing, and observability. - LLM & Generative AI Experience: 3+ years proven experience building and deploying LLM-powered applications, including RAG pipelines, prompt engineering, fine-tuning, and evaluation frameworks. - Agentic AI & Multi-Agent System Design: Hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, Claude, or similar, with the ability to architect and engineer production-grade multi-agent systems. - Solid Foundation in Classic ML: Strong understanding of supervised/unsupervised learning, recommendation systems, reinforcement learning, and model evaluation methodologies. - ML Infrastructure & Tooling Proficiency: Experience with Kubernetes, Docker, Databricks, MLflow, Vector databases, and cloud platforms (AWS, GCP, or Azure). - Technology-Agnostic Mindset & Continuous Learner: A passion for exploring new ideas, staying current with the latest advancements in AI/ML, and solving complex engineering challenges at scale — bringing those insights back to elevate the team. - Strong Communication & Cross-Functional Influence: Excellent communication skills with the ability to align stakeholders, influence technical direction, and drive clarity across engineering, product, and business teams. Benefits - The annual base salary range for this position is $172,350.00 - $212,800.00. - Caring Community: 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: Make an impact on beauty, and feel and see the positive change that your individual voice is a part of. - Health: Choose a healthcare plan with medical, dental, and vision coverage. Sephora fully covers employees’ disability and life insurance. - Wealth: Competitive 401k with 4% match, FSA and HSA programs, and a Student Debt Retirement plan. - Balance: Empowered to find the perfect blend of work/life balance with PTO, flexibility, protected leave, and more. - Growth: Career growth is built into every role, with access to training, development, and tuition reimbursement. - Perks: Enjoy a 30% discount on all merchandise/services, opportunities for free product or “gratis,” and flash sale discounts on LVMH brand products. - Support: Free mental health and financial coaching resources with 24/7 access to Modern Health and Financial Finesse, plus volunteer and donation matching.