Machine Learning Scientist

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building and evaluating GenAI‑ and LLM‑powered solutions and AI agents that improve post‑booking customer experience, including recommendations, customer service, and trip management. Owns end‑to‑end ML and GenAI projects—from problem framing and data preparation through model/agent design, orchestration, deployment, and continuous evaluation. Applies deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust evaluation frameworks (A/B tests, offline metrics, qualitative assessments) that ensure agents are safe, effective, and aligned with business goals. Partners closely with product, engineering, and operations while mentoring junior scientists and helping define best practices for AI agent development and evaluation. Benefits We provide a full benefits package, including travel perks, generous time‑off, parental leave, a flexible work model, and career development resources. Compensation The total cash range for this position in Seattle is $173,000 to $242,500. Employees in this role have the potential to increase their pay up to $277,000 based on performance. The total cash range for this position in SanJose is $187,000 to $261,500. Employees in this role have the potential to increase their pay up to $299,000 based on performance. Starting pay will vary based on location, budget, and individual’s knowledge, skills, and experience. Responsibilities Design & Implement ML Solutions Own the end‑to‑end ML lifecycle for medium‑to‑large projects: problem framing, ideation, research, prototyping, deployment, and post‑launch monitoring. Design robust, scalable ML systems (batch and/or streaming) in partnership with engineering, including data pipelines, feature computation, and model serving. Translate ambiguous business problems into well‑defined ML problems with clear success metrics and validation strategies. Applied Machine Learning & Data Science Develop, evaluate, and iterate on supervised, unsupervised, and deep learning models for prediction, recommendation, and optimization. Apply causal inference and experimental design (A/B testing) to accurately measure impact and guide decision‑making. Read and apply relevant academic and industry research to improve model architectures, training strategies, and evaluation methods. Contribute to defining best practices for experimentation and modeling within the team; raise the technical bar for ML development. Generative AI & Advanced Techniques Build and iterate on models and applications leveraging GenAI / LLM technologies (OpenAI, HuggingFace, Anthropic, Gemini) for customer support, content generation, and workflow automation. Use prompting, retrieval‑augmented generation, and tool/function‑calling patterns to integrate LLMs into production systems. Explore and prototype advanced ML techniques (reinforcement learning, sequence modeling, transformers) where they can provide clear business value. Statistics, Experimentation & Model Design Design end‑to‑end modeling approaches, including data selection, feature engineering, algorithm choice, training procedures, and evaluation. Apply statistical rigor in analyzing experiments and observational data; quantify uncertainty, trade‑offs, and model risk. Define and monitor offline and online metrics that faithfully reflect business goals (customer satisfaction, cost‑to‑serve, operational efficiency). Collaboration, Communication & Visualization Partner closely with product managers, engineers, analysts, and operations to understand requirements, define roadmaps, and align on priorities. Communicate complex technical concepts clearly and concisely to technical and non‑technical stakeholders. Build intuitive dashboards and visualizations to explain model behavior, experiment results, and business impact. Stakeholder & Project Management Lead cross‑functional projects involving multiple partners (product, engineering, operations), driving them from conception to measurable impact. Manage project scope, timelines, and communication, proactively surfacing risks and trade‑offs. Mentor junior scientists and engineers on modeling approaches, experimentation, and analytical problem‑solving. Experience & Qualifications Experience & Education PhD in a quantitative field (Computer Science, Statistics, Mathematics, Physics, Economics, Operations Research) and ~3+ years of industry experience; or Master’s degree in a quantitative field with ~5+ years of relevant industry experience. Proven track record of building and deploying ML models that meaningfully impact business metrics in a production environment. Functional & Technical Skills Applied ML & Statistics Strong knowledge of machine learning theory and practice (supervised learning, representation learning, ranking/recommendation, deep learning). Solid grounding in statistics, experimental design (A/B testing), and basic causal inference; comfortable designing and analyzing online experiments. Able to design end‑to‑end ML solutions: frame the problem, choose data sources, select algorithms, define evaluation strategies, and iterate based on results. Engineering & Tooling Strong programming skills in Python and its data/ML ecosystem (pandas, scikit‑learn, PyTorch/TensorFlow, PySpark), plus proficiency in SQL. Experience with cloud‑based data/compute platforms and modern data/ML tooling (Spark, Airflow, feature stores, model serving frameworks). Follow software engineering best practices (version control, code reviews, testing, documentation) and contribute to shared libraries and tooling. Generative AI & Advanced Methods Hands‑on experience using GenAI / LLM APIs (OpenAI, HuggingFace, Anthropic, Gemini) in prototypes or production is highly desired. Familiarity with concepts like prompt engineering, retrieval‑augmented generation, function/tool calling, and evaluation of LLM‑based systems. Experience with reinforcement learning, bandits, or other advanced ML techniques is a plus. Problem Solving & Communication First‑principles problem solver: able to decompose ambiguous problems, identify key assumptions, and design pragmatic, iterative solutions. Excellent written and verbal communication skills; able to tell a compelling story with data and models and influence decisions. Collaborative and customer‑obsessed, balancing scientific rigor and engineering pragmatism in a product environment. Highly Desired Experience Domain experience in customer service, recommendations, personalization, or e‑commerce applications. Experience building ML systems for operational decision‑making (contact routing, triage, capacity/effort prediction, workflow optimization). Experience mentoring other scientists or engineers and contributing to technical culture (brownbags, tech talks, documentation, best practices). Legal and Accessibility Accommodation requests: If you need assistance with any part of the application or recruiting process due to a disability or other condition, please reach out to our Recruitment Accommodation Team. Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. This employer participates in E‑Verify. Tagged as: Industry, Machine Learning, NLP, Supervised Learning, United States (text only). #J-18808-Ljbffr