Senior Applied ML Engineer, Search & Relevance

Job Description We're looking for an Applied Machine Learning Engineer to design and deliver intelligent typographic features across Adobe's creative products, simplifying decisions and workflows for millions of users. Typography has been core to Adobe for nearly 40 years, and our mission is to make advanced typographic capabilities simple smart, and inspiring. The Adobe Fonts & Type team builds systems that surface the power of fonts and automate complex layout and styling. In this role you will apply advanced modeling to design, train, debug, and operationalize ML systems for layout, generative styling, and design intelligence. You will own the modeling lifecycle end-to-end: from data preparation and training pipelines to production deployment and monitoring, and partner closely with software engineers to integrate models into hybrid product architectures. Why this role matters This role brings modeling into production at scale for one of Adobe's core creative domains. You will help move ML systems from prototype to reliable product capabilities that improve the creative experience for millions of users. What You’ll Do Model typographic layout, styling, and generative workflows; convert research ideas into reproducible implementations and product features. Build and maintain end-to-end training pipelines including data ingestion, feature engineering, training, validati [... source excerpt omitted ...] rameters for performance and robustness. Optimize training and inference (mixed precision, quantization, distillation, pruning) with attention to latency, cost, and scalability. Productionize models and inference services; implement monitoring for model drift and data quality. Collaborate with computer science and product engineers to integrate modeling solutions into client and server architectures (APIs, SDKs, services). Write production-quality code, tests, and automation for labeling and evaluation; follow CI/CD best practices. Prototype generative approaches (diffusion, GANs, multimodal transformers) and apply reinforcement learning or bandit methods where appropriate for [... source excerpt omitted ...] p across the modeling lifecycle: data -> modeling -> training -> deployment -> monitoring. Experience debugging models, tuning hyperparameters, and improving model performance in production environments. Experience building data pipelines and model serving infrastructure. Strong software engineering fundamentals (testing, version control, code reviews, CI/CD). Experience working with engineering teams to integrate modeling solutions into large-scale product systems. Comfortable in an R&D and iteration-heavy environment and able to move prototypes toward reliable production systems. Nice to Have Experience with diffusion models, GANs, or reinforcement learning. Computer visi