Machine Learning Engineer III, ML Operations

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Introduction to the Team We create and deliver an aligned, dedicated marketing strategy to fuel each Expedia Group brand's success. Since our travelers interact with us through our brands, we maintain a brand-focused approach in our marketing while leveraging the scale and efficiency we’ve built through functional expertise. The Meta/SEM Bidding Programs team at Expedia Group is looking for a Machine Learning Engineer III who mentors junior engineers, applies modern data and ML engineering principles to improve existing systems, and leads complex, well-defined projects in a high-scale production environment. In this role, you will: Collaborate with peers and stakeholders across the organization to understand cross-dependencies, shape solutions, and translate experimental DS workflows into robust production pipelines. Develop, refactor, and test complex ML and software components, applying solid software engineering practices (design principles, data structures, design patterns) to produce clean, maintainable, and optimized code. Contribute to the design of big data and ML applications , including how models are trained, evaluated, and served at scale across batch and streaming (online) inference workflows. Evaluate, monitor, and operate ML models in production , instrumenting pipelines to capture online and offline metrics (latency, throughput, accuracy/quality, drift, and business KPIs) and using them to drive iteration. Diagnose model and pipeline issues (e.g., performance regressions, instability), distinguish data drift vs. model drift , and drive mitigations such as retraining, recalibration, and feature or architecture changes. Design and implement guardrails for production models (safety constraints, thresholds, fallbacks, safe defaults) to protect customer and business outcomes and improve reliability. Use AI as a co-pilot across the software and ML engineering lifecycle—to clarify requirements, accelerate coding, strengthen automated tests, support model evaluation, and improve delivery—while applying generative AI and large language model (LLM) techniques responsibly where they add value. Identify and address areas of inefficiency in code, model architectures, and system operations (including memory/compute efficiency), recommending and implementing improvements to observability, policies, and processes. Experience and Qualifications: 5+ years of relevant professional experience with end-to-end machine learning engineering pipelines in production (feature engineering, model training, validation, deployment, scoring, monitoring, and iteration), including streaming applications in hybrid/cloud environments. Bachelor’s or Master’s degree in a technical field (e.g., Computer Science) or equivalent relevant work experience. Strong command of Spark (or similar big data frameworks), including evaluating, optimizing, and debugging large-scale data processing applications. Proficiency with ML libraries such as PyTorch and/or TensorFlow, and experience integrating models into production services for inference at scale. Solid ML fundamentals with working knowledge of deep learning and big data concepts, and demonstrated experience refactoring and scaling ML models for production