Lead Engineer, Machine Learning

Job
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

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:

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%)
* *
* What You'll Bring:

* 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 Lang Chain, Lang Graph, 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…