Senior Machine Learning Engineer, Express AI Foundations

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 Express enables all users, whether individuals or large organizations, to effortlessly produce impressive content. The AI Foundations team constructs a flexible, scalable AI framework that drives creativity at scale in design, imaging, motion, and personalization. We're looking for an engineer to build and implement the AI framework for Adobe Express, merging strong ML skills with proficiency in distributed systems, data architecture, and large-scale service development. You'll contribute to building systems with intelligent behavior, reasoning workflows, and production-quality ML systems that creators interact with every day. Your work will cut through layers of the end-to-end foundation that brings Agentic AI, Create AI, Imaging AI, Motion AI, and Personalization AI to life — spanning model orchestration, inference systems, data pipelines, caching and storage layers, session analytics, and continuous evaluation frameworks. What You’ll Do Contribute to hands on development for building the complete AI stack for Adobe Express — covering Agentic AI, Construct AI, Imaging AI, Motion AI, and Personalization AI. Develop and operationalize end-to-end systems — integrating microservices, data pipelines, LLM orchestration layers, in-house and third-party models, databases, caches, session analytics, and evaluation systems into a cohesive architecture. Develop large-scale data and inference infrastructure to support model training, fine-tuning, evaluation, and deployment — employing Spark, Kafka, Flink, and other distributed frameworks. Develop high-performance runtime services for inference and orchestration with strong observability, fault tolerance, and latency guarantees. Apply strong caching and storage tactics to enhance efficiency and cost-effectiveness for various AI workloads. What You’ll Bring 5+ years of experience in large-scale distributed systems AI infrastructure, or ML platform engineering. Proven expertise in building and scaling data pipelines, real-time streaming systems, and event-driven architectures (Kafka, Spark, Flink, etc.). Strong background in API development, caching strategies, database development, and performance optimization for large-scale serving systems. Hands-on experience with LLM orchestration frameworks, model routing, and multi-model inference. Proficiency in Python, Java, C++, or Go, with an emphasis on distributed systems, cloud-native deployment, and performance tuning. Familiarity with Agentic AI patterns — reasoning loops, memory persistence, task decomposition, and multi-agent coordination. Strong communication and collaboration skills, with experience influencing cross-functional technical direction. Preferred Qualifications Bachelor's or equivalent experience in Computer Science, Data Science, Machine Learning, or a related technical field. Experience building large scale high throughput / low latency applications backed by ML models to build workflows Exposure to Generative AI (LLMs, diffusion, or multimodal architectures). Experience with MLOps pipelines, feature stores, and model registries. Why Adobe At Adobe, we’re crafting the future of creativity through intelligence. The AI Foundations team combines the agility of a startup with the scale and mission of Adobe — constructing the AI stack for Ad ... (truncated, view full listing at source)