Expedia Group is looking to advance Ranking, Recommendations, Text Search, and Traveler Insights across its brands by developing ML-powered features that enhance the traveler experience and optimize key business metrics.
Requirements
- Comfortable programming in Python and Experience developing software in at least 3 languages, with a strong command of at least one language across multiple tech stacks; able to choose the right language for the task.
- Built and maintained at least one end-to-end ML pipeline in production, covering feature engineering, model training, validation, and scalable inference.
- Basic knowledge in PyTorch, TensorFlow, and common patterns for model serving; familiar with dependency management in ML/DS systems.
- Strong command of Spark (including map-reduce); experience training ML models on large datasets with GPUs or distributed compute.
- Experience of using cloud services (e.g. AWS). Experience with workflow orchestration tools (e.g. Airflow). Experience building streaming applications in cloud/hybrid environments; familiar with IAM policies and cloud storage (e.g., S3).
- Applies data structures, design patterns, and software frameworks to write clean, modular, maintainable code.
- Designs testable systems, uses tools to monitor/debug code, and fixes issues within SLA constraints.
Responsibilities
- Designs and develops moderately complex, testable, and observable systems; contributes to architectural discussions, especially for big data and ML applications.
- Builds APIs and services for use across business units, ensuring reusability and scalability.
- Identifies inefficiencies in code and systems; proposes improvements and innovations in ML engineering, platforms, and tooling.
- Ensures performance, scalability, and reliability of systems through monitoring, testing, and adherence to SLAs.
- Performs code reviews and contributes to a culture of engineering excellence and best practices.
- Documents and presents findings (e.g., RCAs or tech deep dives) clearly to both technical and non-technical audiences.
- Applies systems thinking to identify process or policy improvements that scale beyond immediate teams or projects.
Other
- Internship or professional experience in software engineering or machine learning engineering
- Understands how technical work supports business goals; identifies and solves project-level business problems with minimal guidance.
- Coordinates with stakeholders across engineering, product, and data to align priorities and deliver shared solutions.
- Stays current with emerging technologies, seeks new skills, and shares knowledge across teams.
- Participates in communities of practice, promotes shared learning, and fosters a culture of knowledge exchange.
