Machine Learning Engineer III

Private Label Solutions (PLS) is the B2B arm of Expedia Group. We bring Expedia Group's innovative technology and distribution solutions to partners across the world. These businesses include global financial institutions, corporate managed travel, offline travel agents, global travel suppliers (like major airlines) and many more …

We are looking for a Machine Learning Engineer III to join the Private Label Solutions (PLS) Machine Learning Engineering. You will be part of a team that designs, builds, and maintains the infrastructure for deploying ML solutions, managing data pipelines, and optimizing compute resources for our ranking problems, recommendation engines, and pricing optimization systems. Your work will be crucial in ensuring our ML models are efficiently deployed, scaled, and monitored in production environments, contributing substantial value to our partners and EG.

We're seeking a passionate and experienced ML engineer to join our Revenue Optimization ML team at Expedia Group. This high-impact role combines infrastructure expertise with applied ML knowledge, requiring proficiency in software engineering, distributed systems, and machine learning operations (MLOps). You will be responsible for architecting and implementing robust ML systems, collaborating with data scientists to streamline the model development to production pipeline, and optimizing our ML infrastructure for performance and scalability.

This role is well-suited to someone with a solid foundation in at least one of the following areas: software engineering, data engineering, distributed systems, or applied machine learning and a readiness to grow into others as the work requires. Experience or exposure to one or more of the following is valuable: deploying models to production, managing training data pipelines, or optimizing compute resources for low latency inference. We’re looking for someone who can quickly adapt to new technologies, understand the broader context of our ML systems, and deliver high-quality solutions efficiently. While this is primarily an individual contributor role, you will also be expected to mentor junior team members and provide technical guidance that drives both business impact and team growth.

We welcome candidates who are strong, language-agnostic problem solvers and passionate about building robust ML systems, even if you don’t meet every single requirement listed below. If you believe you can do the job and are excited to grow with us, we’d love to hear from you.

In this role, you will:

• Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments.

• Build and maintain efficient data pipelines for large-scale processing and feature engineering.

• Optimize compute resources and improve model serving performance across ML systems.

• Implement robust monitoring, logging, and alerting systems, and contribute to ML Ops practices including CI/CD pipelines.

• Collaborate closely with ML scientists to streamline the model development-to-production workflow.

• Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders.

Experience and qualifications:

• You hold a Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field.

• You have 3+ years of experience in Software, Data, or ML engineering roles (preferred).

• You bring strong problem-solving skills, proficiency in Python, and experience with ML frameworks like TensorFlow or PyTorch.

• You understand ML algorithms, model architectures, and enjoy building scalable, reliable ML systems.

• You have exposure to cloud platforms (e.g., AWS, GCP), containerization (Docker, Kubernetes), and scalable data systems (e.g., Spark, Kafka).

• You are familiar with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow), and collaborate well across teams.