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. - Architect & Engineer Production-Grade AI/ML Systems: Design, build, and maintain scalable ML and Agentic AI systems using established engineering design patterns. - Own End-to-End ML Solutions: Engineer and own batch and real-time model serving, agentic pipelines, RAG systems, and LLMOps infrastructure. - Establish & Optimize ML Pipelines: Build scalable, efficient, and automated pipelines for data processing, feature engineering, model development, validation, evaluation, and deployment. - Deliver High-Quality Code in a Continuous-Release Environment: Write clean, efficient, and well-structured code to deliver AI/ML products iteratively. - Partner Cross-Functionally to Shape AI/ML Capabilities: Collaborate closely with Product, Engineering, Data Scientists, ML Engineers, and Business stakeholders. - Drive Delivery Planning & Engineering ROI: Review and prioritize epics and projects with clear breakdown, dependency management, and delivery planning. - Mentor, Grow & Inspire the Team: Mentor and develop ML Engineers and Data Scientists by promoting best practices in ML engineering. Qualifications - Deep ML Engineering Expertise: 5+ years hands-on experience in model development, training pipelines, feature stores, model serving, and MLOps/LLMOps. - Strong Software Engineering Fundamentals: 8+ years proficiency in Python, distributed systems, API design, and cloud-native architectures. - LLM & Generative AI Experience: 3+ years proven experience building and deploying LLM-powered applications. - Agentic AI & Multi-Agent System Design: Hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, Claude, or similar. - 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 and staying current with the latest advancements in AI/ML. - Strong Communication & Cross-Functional Influence: Excellent communication skills with the ability to align stakeholders and influence technical direction. 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. - Fulfilling Path: Opportunities that will challenge, stretch, and develop your skills. - Meaningful Work: Make an impact on beauty and see the positive change. - Health: Choose a healthcare plan with medical, dental, and vision coverage. - Wealth: Competitive 401k with 4% match, FSA, HSA programs, and Student Debt Retirement plan. - Balance: Empowered to find the perfect blend of work/life balance with PTO and flexibility. - Growth: Access to training, development, and tuition reimbursement. - Perks: 30% discount on merchandise/services and opportunities for free products. - Support: Free mental health and financial coaching resources with 24/7 access. Company Description 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 to applicants with disabilities or other medical conditions. Join Us and Belong to Something Beautiful.

Role Description We’re looking for an exceptional Senior AI Engineer with 4+ years of software engineering experience and a proven track record delivering Generative AI solutions to join Modus’s Engineering Consulting team. No two days look the same in this role. You might spend one day white boarding an architecture with a client, the next drafting a proposal for a new engagement, and the next deep in code shipping an LLM-powered feature. As a senior engineer on our consulting team, you'll move fluidly between: - Designing and building AI solutions: scoping, architecting, and implementing applications powered by generative AI, from proof of concept to production deployment. - Working directly with clients: understanding their problems, proposing solutions, and translating business needs into technical plans. You're the trusted technical advisor in the room. - Hands-on engineering: writing production-quality code, integrating LLMs and ML models into real systems, and deploying them to the cloud. - Collaborating across disciplines: partnering with cloud architects, DevOps specialists, and front-end developers to deliver end-to-end solutions. - Supporting business development: contributing to proposals, scoping engagements, and supporting sales and marketing efforts when needed. Qualifications - Experience deploying and operationalizing AI. - Hands-on experience deploying large and small language models (LLMs/SLMs) in the cloud and on-premises. - Comfortable with techniques like prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and agentic architectures. - Understanding of how to evaluate model outputs and build reliable systems around inherently probabilistic components. - Familiarity with classical ML (prediction, classification) and data science fundamentals is a desirable plus. - A background in cloud services for machine learning, text extraction, speech recognition, or computer vision will broaden the engagements you can contribute to. - Proficiency in Python is a must. - Experience with other programming languages like Java, Scala, Rust, or R is highly desirable. - Knowledge and experience in cloud architecture, DevOps, and front-end development would help in collaboration. - Comfortable presenting work to both technical and non-technical audiences. - Experience in supporting sales and marketing efforts is a desirable plus. Requirements - LLM deployment and agentic AI: experience with LLM orchestration frameworks (e.g., LangChain, LangGraph, Google ADK), agent tooling and protocols (e.g., MCP, function calling), and evaluation/observability libraries. - Cloud AI services: experience with managed AI services such as Amazon Bedrock, SageMaker, Rekognition, etc. or equivalents on other platforms. - Machine learning frameworks: familiarity with frameworks like TensorFlow or PyTorch is a plus. - AI-assisted development tools: experience with AI-powered coding assistants (e.g., GitHub Copilot, Cursor, Claude Code) and agentic coding workflows. - Cloud platforms: solid experience with at least one major cloud provider (AWS preferred); infrastructure as code (e.g., Terraform) is a plus. - Version control and DevOps: Git, GitHub/GitLab; DevOps and CI/CD experience is a big plus. - Programming and scripting languages: Python, Bash; knowledge of other languages is a plus. - Data storage and querying: SQL and NoSQL databases. - Curiosity and drive to keep learning in the rapidly-evolving field of AI. Benefits - 100% remote since day one. - Named a top company for remote work by FlexJobs and Inc. - Culture of autonomy, mastery, inclusion, and continuous learning. - Part of a high-performing group that leverages each other’s strengths and celebrates every success together.

• Design, develop, and architect high-performance AI/ML services and pipelines in a cloud-based environment. • Contribute to cutting-edge R&D initiatives, including service integration with emerging frameworks and platforms. • Assist with the technical resolution of issues on mission-critical production systems, embracing the philosophy: "you build it, you own it, you run it." • Think outside the box to devise solutions that scale, are fault-tolerant, easy to grasp, and elegant.

• Develop large-scale distributed machine learning systems that are scalable, performant, efficient, and reliable • Collaborating with cross-functional teams to help deploy/integrate machine learning models. • Liaise with the BUs for their ML needs and work on the cross-BU ML portfolio. • Optimize feature extraction, transformation and selection. • Work with and manage Feature Stores for reusability across ML pipelines. • Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform • Contribute to evaluating and adopting new technologies and tools to enhance our machine-learning capabilities