Machine Learning Engineer 5

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! The Opportunity Adobe is seeking a Senior Machine Learning Engineer to lead the development of next-generation personalization models and intelligent AI agents within Adobe Experience Platform (AEP). Our team is building intelligent agent systems that can autonomously design, generate, optimize, and evaluate personalization models at scale. These agents reason over customer data, business objectives, and experimentation signals to create adaptive models that continuously improve digital experiences. As a senior member of the AI team, you will own the end-to-end lifecycle of personalization AI systems, from research and modeling to production deployment, monitoring, and MLOps automation. You will work at the intersection of personalization modeling, multimodal LLMs, agentic reasoning, real-time decisioning, and enterprise-scale ML infrastructure. This role is ideal for engineers and applied scientists who want to build production-grade AI systems that autonomously power personalization, targeting, ranking, and next-best-action strategies across billions of interactions. You’ll collaborate closely with Adobe Research, Product Management, and Platform Engineering to ensure AI-driven personalization is not an add-on—but a foundational capability embedded across Adobe’s Digital Experience products. What You’ll Do Design and build advanced personalization models including propensity models, recommendation models and systems, uplift models, reinforcement learning, and generative personalization systems. Develop LLM-powered intelligent agents that can generate and tune personalization models, automate experimentation and model optimization and recommend next-best-actions and targeting strategies Architect and implement end-to-end ML systems, including: Feature engineering pipelines (batch and streaming) Model training and evaluation frameworks Low-latency inference systems Build and scale real-time decisioning systems that operate across high-throughput enterprise environments. Lead MLOps initiatives, including CI/CD for ML, model versioning, monitoring, drift detection, automated retraining, and performance governance. Drive system reliability, scalability, and observability across distributed ML services. Partner with product leaders to translate personalization strategy into measurable business impact. Mentor engineers on modern ML practices, agentic AI design, and production grade ML architecture. Champion responsible AI and personalization practices, focusing on interpretability, fairness, safety, and user trust. What You Need to Succeed Bachelor’s degree with 8+ years of experience, or PhD with 5+ years building and deploying ML systems at scale. Deep expertise in personalization systems, recommendation models, or real-time decisioning architectures. Some experience with LLMs, agentic systems, prompt engineering, RAG, or context engineering, especially in production environments. Proven success building and shipping end-to-end ML systems, from research to deployment and ongoing optimization. Hands-on experience with MLOps best practices, including model lifecycle management, monitoring, automated retraining, CI/CD for ML, and large-scale inference systems. Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, HuggingFace, LangChain, or equivalent. S ... (truncated, view full listing at source)