Machine Learning Scientist III

Introduction to the Team 

Expedia Technology teams partner with our Product teams to create innovative products, services, and tools that deliver high-quality experiences for travelers, partners, and employees. A singular technology platform powered by data, machine learning, and AI provides secure, differentiated, and personalised experiences that build loyalty and traveler satisfaction.

The Content AI team develops machine learning capabilities that improve the quality, relevance, and usefulness of content-focused products and services across Expedia Group. Working within the Product & Technology Machine Learning Science and Engineering organisation, you will translate research and experimentation into reliable, scalable production outcomes. The team works across content understanding, review intelligence, search relevance, ranking, recommendations, content moderation, image-based AI applications, and Generative AI experiences used by millions of travelers worldwide.

In this role, you will: 

• Develop, evaluate, and improve machine learning models and algorithms for Content AI applications.

• Design and implement end-to-end machine learning solutions, including data preparation, feature engineering, model training, validation, deployment, monitoring, and continuous optimisation.

• Apply statistical analysis, experimentation, and data-driven decision-making to assess model performance and guide technical direction.

• Collaborate with engineers, product managers, applied scientists, and domain experts to translate ambiguous business problems into scalable ML solutions.

• Contribute to system design, low-level design, API development, and data modelling for production machine learning systems.

• Build and operate production solutions using technologies such as Python, TensorFlow, PyTorch, Redis, Airflow, Docker, Kubernetes, and cloud-native infrastructure.

• Design and deploy scalable batch and real-time ML pipelines supporting customer-facing applications.

• Develop and operationalise LLM applications, RAG systems, and Generative AI capabilities for content understanding, summarisation, classification, and recommendation use cases.

• Build evaluation frameworks and observability solutions to monitor model quality, reliability, latency, drift, and business impact.

• Evaluate trade-offs between model quality, scalability, latency, and infrastructure cost when developing AI-powered products.

• Partner with engineering teams to deploy and scale machine learning systems in production.

• Produce clear technical documentation and share expertise through mentorship, design reviews, and technical leadership.

Experience and Qualifications 

• Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Engineering, Data Science, or a related technical field; or Equivalent related professional experience.

• 5+ years of industry experience developing machine learning solutions in production environments.

• Proficient programming and software engineering skills in Python, including experience building production-grade services and APIs.

• Experience with machine learning frameworks such as TensorFlow and/or PyTorch.

• Experience owning machine learning solutions across the full lifecycle, including experimentation, evaluation, deployment, monitoring, and operational improvement.

• Experience building and operating production ML systems at scale.

• Understanding of distributed systems, data structures, algorithms, system design, low-level design, API design, and data modelling.

• Experience with batch and real-time data processing systems, databases such as Redis, and low-latency serving technologies such as KServe, FastAPI.

• Ability to partner effectively with cross-functional teams to deliver measurable machine learning outcomes.

Preferred qualifications 

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Data Science, or a related STEM field.

• Experience applying Generative AI and LLMs to customer-facing products and designing, building, or scaling RAG systems.

• Experience in search, ranking, recommendations, personalisation, content intelligence, moderation, NLP, computer vision, or related AI domains.

• Experience with Airflow or comparable workflow orchestration platforms.

• Experience with cloud-native environments and container technologies, including Docker and Kubernetes.

• Experience developing model evaluation frameworks and AI quality measurement systems.

• Experience making architectural decisions across multi-service machine learning environments.

• Experience balancing model quality, operational cost, scalability, and performance requirements for production AI systems.