Overview

Seeking a Machine Learning Engineer III to enhance systems and mentor junior engineers at Expedia Group.

Responsibilities

• Collaborate with peers and stakeholders across the organization to understand cross-dependencies, shape solutions, and translate experimental DS workflows into robust production pipelines.
• Develop, refactor, and test complex ML and software components, applying solid software engineering practices (design principles, data structures, design patterns) to produce clean, maintainable, and optimized code.
• Contribute to the design of big data and ML applications, including how models are trained, evaluated, and served at scale across batch and streaming (online) inference workflows.
• Evaluate, monitor, and operate ML models in production, instrumenting pipelines to capture online and offline metrics (latency, throughput, accuracy/quality, drift, and business KPIs) and using them to drive iteration.
• Diagnose model and pipeline issues (e.g., performance regressions, instability), distinguish data drift vs. model drift, and drive mitigations such as retraining, recalibration, and feature or architecture changes.
• Design and implement guardrails for production models (safety constraints, thresholds, fallbacks, safe defaults) to protect customer and business outcomes and improve reliability.
• Use AI as a co-pilot across the software and ML engineering lifecycle—to clarify requirements, accelerate coding, strengthen automated tests, support model evaluation, and improve delivery—while applying generative AI and large language model (LLM) techniques responsibly where they add value.
• Identify and address areas of inefficiency in code, model architectures, and system operations (including memory/compute efficiency), recommending and implementing improvements to observability, policies, and processes.

Qualifications

• 5+ years of relevant professional experience with end-to-end machine learning engineering pipelines in production (feature engineering, model training, validation, deployment, scoring, monitoring, and iteration), including streaming applications in hybrid/cloud environments.
• Bachelor’s or Master’s degree in a technical field (e.g., Computer Science) or equivalent relevant work experience.
• Strong command of Spark (or similar big data frameworks), including evaluating, optimizing, and debugging large-scale data processing applications.
• Proficiency with ML libraries such as PyTorch and/or TensorFlow, and experience integrating models into production services for inference at scale.
• Solid ML fundamentals with working knowledge of deep learning and big data concepts, and demonstrated experience refactoring and scaling ML models for production (latency, throughput, and memory footprint).
• Hands-on experience evaluating and monitoring ML models in production, including metric and alert design, feedback loops, and basic MLOps practices (CI/CD for ML, experiment tracking, model registry, deployment and observability tools).
• Familiarity with secure data access and governance (e.g., IAM policies for S3, access patterns for training and serving) and with designing moderately complex distributed systems centered on ML training and serving.
• Working knowledge of generative AI and LLMs (e.g., prompting, RAG, fine-tuning, embeddings/vector stores, evaluation) and their responsible, production-grade application is strongly preferred.
• Hands-on experience using AI-assisted engineering tools (e.g., GitHub Copilot, Claude Code, or equivalent) across the software and ML development lifecycle, consistent with Expedia Group's expectation that engineers actively build and apply AI skills.

Nice to have

• Experience with secure data access.
• Familiarity with generative AI.

Education

• Bachelor’s or Master’s degree in a technical field.

Benefits

• Flexible work environment.
• Career growth opportunities.