Lead Machine Learning Engineer

About the role

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.

Responsibilities

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

Requirements

• 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.

Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.

• Experience: 5+ years of hands-on experience in machine learning and software engineering.

• Technical Skills: Proficiency in Python, experience with cloud platforms (AWS, GCP, or Azure), and familiarity with ML libraries and tools like TensorFlow, PyTorch, and Scikit-Learn.

Skills

• Machine Learning

• Software Engineering

• Cloud Computing

• AI/ML Frameworks

• Agentic AI

• Data Processing

• Model Serving

• MLOps/LLMOps

Benefits

The annual base salary range for this position is $191,520 - $212,800. Actual base salary offered depends on a variety of factors, including but not limited to, 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.

Pay

The annual base salary range for this position is $191,520 - $212,800. Actual base salary offered depends on a variety of factors, including but not limited to, 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.

Schedule

This position is currently available for a hybrid schedule with a mix of remote and in-office work.