Machine Learning Engineer II

Requirements

• A degree in software engineering, computer science, informatics, or a similar field.
• 3+ years of professional experience with a Bachelor's degree, or 2+ years with a Master's degree.
• Must have experience in big data technologies, particularly Spark, Hive, and Databricks.
• Proficiency in Python and experience developing and deploying Batch and Real-Time Inferencing applications.
• Good understanding of prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and LLM orchestration frameworks.
• Experience programming in Scala.
• Hands-on experience with OOAD, design patterns, SQL, and NoSQL.
• A good understanding of machine learning pipelines and the ML Lifecycle.
• Experience integrating GenAI solutions with enterprise systems, APIs, and data platforms to solve business problems.
• Experience using cloud services (e.g., AWS) and workflow orchestration tools (e.g., Airflow).
• Familiarity with the basics of both traditional Machine Learning and Generative-AI algorithms and tools.
• Accommodation requests
• If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request .
• We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for award-winning culture by organizations like Forbes, TIME, Disability:IN, and others.
• Expedia Group's family of brands includes: Brand Expedia®, Hotels.com®,

Benefits

Additional Information

Expedia 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. Why Join Us? To 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. We 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. Introduction to team : Our Supply and Market Place division is sourcing the best possible inventory and content from our partners, generating the best prices and customer experience, and ensuring our supply is transacted fairly across our marketplace. This division builds innovative products, services, and tools to deliver high-quality experiences for partners and travellers both. The goal of Supply Coaching Foundation org is to delight partners by connecting them to the right travellers. We'll do that by building an adaptive experience that provides data and ML driven opportunities to our partners to help them grow their business. As part of NBA-Scout team we computes, organizes and streams the recommended actions for EG's supply partners with the ultimate goal of maximizing the returns for their time investment on Expedia Marketplace. Plus we also tracks partner's reactions to these recommendations to continuously learn & evolve. Our team works very closely with Machine Learning Scientists and Software Engineers in a fast-paced Agile environment to create and productionize algorithms that directly impacts the partners of Expedia. Along with this increased demand for creating new recommendations, we aim to build the NBA platform to make recommendation creation faster, easier, and more scalable. In this role, you will: Work in a cross-functional geographically distributed team of Machine Learning engineers and ML Scientists to design and code large scale batch and a few real-time data pipelines on the Cloud. Prototype creative solutions quickly by developing minimum viable products and work with seniors and peers in crafting and implementing the technical vision of the team Actively participate in all phases of the end-to-end ML model lifecycle (includes feature engineering, model training, model scoring, model validation) for enterprise applications projects to tackle sophisticated business problems in production environments Collaborate with global team of data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems. Participate in code reviews to assess overall code quality and flexibility. Passionate about simplifying systems, processes, and architecture while maintaining high quality and reliability. Define, develop and maintain artifacts like technical design or partner documentation Maintain, monitor, support and improve our solutions and systems with a focus on service excellence

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