Ph3Why Join Us? /h3pExpedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. /ppTo shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. /ppWe provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees\' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us. /ph3Introduction to the team /h3pThe Unified Personalisation Service team is part of Expedia Product Technology. UPS is building Expedia Group\'s centralized, real-time personalisation engine across brands and channels, powering ranking, recommendations, retrieval, and other adaptive experiences that help travelers see more relevant, contextual, and useful experiences throughout their journey. /ppWe are looking for a Machine Learning Scientist III to help build production ML systems for personalisation, with emphasis on deep learning, neural recommender systems, sequential and session-based modelling, embeddings, scalable experimentation, and reliable model deployment. /ppThis is a hands-on applied science and engineering role for someone who can contribute across model development, experimentation, data pipelines, deployment, and production model quality. /ph3In this role, you will /h3ulliDevelop, apply, and advance machine learning solutions for personalisation use cases, translating business and customer problems into scalable scientific approaches and production-ready models. /liliDesign experiments, evaluate model performance, and use data-driven methods to improve relevance, ranking, recommendation, and overall customer experience across personalisation systems. /liliPartner across engineering, product, analytics, and science teams to define solution approaches, influence technical direction, and deliver ML capabilities that can operate across multiple products and domains. /liliContribute technical depth in model development, feature design, data preparation, offline and online evaluation, and the operationalisation of machine learning solutions in production environments. /liliApply strong technical judgment to system design, API design, data modelling, and low-level solution design that support robust, maintainable, and extensible ML-powered services. /liliSafely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products. /li /ulh3Minimum Qualifications /h3ulliBachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent professional experience. /lili5+ years of relevant experience in machine learning, applied science, data science, or software development, including delivering production-grade ML solutions. /liliDemonstrated ownership of machine learning solutions within a service, multi-service, or domain-level scope, with accountability for model quality, experimentation, and operational performance. /liliStrong foundation in machine learning methods, statistical analysis, experimentation, feature engineering, and working with large-scale datasets in production environments. /liliProficiency in software engineering practices for scientific systems, including coding, low-level design, API design, data modelling, and collaboration with engineering teams to productionise solutions. /li /ulh3Preferred Qualifications /h3ulliAdvanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field. /liliExperience building and scaling personalization, recommendation, ranking, retrieval, or relevance models in large, complex consumer-facing environments. /liliExperience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, or representation learning at scale. /liliExperience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, or retrieval-augmented personalisation workflows. /liliDemonstrated ability to use data, metrics, and experimentation to guide prioritisation and decision-making while balancing scientific rigour, product impact, and platform scalability. /liliExperience with production ML workflows such as model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps. /li /ul /p #J-18808-Ljbffr