Senior Machine Learning Engineer

Senior ML/Gen AI Engineer

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

We are the Strategic Partnerships & Affiliates team in the Expedia Product & Technology division of Expedia Group. We are building the next-generation, scalable B2B partnership platform that will power hundreds of thousands of demand partners across the industry ranging from big businesses and Enterprises to small bloggers, micro influencers and creators in helping them recommend Expedia Group brands to their audiences and in the process grow their businesses. We aim to redefine the travel partnerships sector by building innovative partner tools and solutions that incorporates the new ways in which today's travelers discover and shop travel products. To do this, we need technically passionate engineers with an entrepreneurial approach who love challenges, enjoy problem solving and take pride in delivering best-in-class products. You will work with a geo-distributed, cross functional team of 50+ engineers designing and developing solutions for complex problems with a wide-reaching business impact.

In this role, you will:

• Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
• Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring
• Lead design discussions and architecture reviews; drive high-impact engineering decisions for ML platforms and applications
• Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
• Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
• Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
• Ensure scalability, observability, and fault-tolerance across all components of the ML stack
• Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure-as-code, and monitoring
• Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
• Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes

Minimum Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• 8+ years of experience in software/ML engineering with a proven track record of delivering ML solutions at scale  
• Strong programming skills in modern languages such as Python, Scala, or Java
• Deep experience in building and maintaining production-grade ML pipelines and infrastructure
• Expertise in MLOps practices, including model lifecycle management, versioning, monitoring, and CI/CD for ML
• Experience with big data ecosystems (e.g., Spark, Hive, Databricks, Delta Lake) and streaming technologies
• Proficient in working with ML frameworks like TensorFlow, PyTorch, XGBoost, or similar
• Experience working in cloud-based environments (AWS, GCP, or Azure) and with infrastructure-as-code tools
• Hands-on experience with orchestration tools like Flyte, Airflow, Kubeflow, etc.
• Proficient in containerization and orchestration technologies like Docker and Kubernetes

Preferred Qualifications:

• Familiarity with advanced ML techniques, including deep learning, NLP, recommendation systems, and generative AI  
• Experience designing or implementing multi-agent architectures for autonomous collaboration and decision-making 
• Understanding of agent planning, memory, tool use, and self-reflection mechanisms
• Experience building basic ML models
• Experience with automated testing across different layers (unit, integration, functional)