Machine Learning Engineer II

Chewy is seeking a Machine Learning Engineer II to join our Marketing Science and Operations Tech team to help design, build, and scale machine learning systems that power our marketing optimization and personalization capabilities. As an MLE II, you will take on meaningful ownership within the ML lifecycle—supporting experimentation, building production-grade pipelines, and collaborating across data science and engineering to bring models to life in Chewy’s marketing ecosystem.

This role is ideal for engineers with a strong technical foundation in machine learning and distributed data processing who are ready to take on increased responsibility, drive improvements in ML workflows, and contribute to best practices across the team. You’ll work in a fast-paced, highly collaborative environment where learning, mentorship, and impactful engineering are core to the experience.

What You'll Do:

• Design, build, and optimize data pipelines and ML workflows supporting model training, validation, deployment, and monitoring
• Partner with Data Scientists to productionize models, ensuring scalability, reliability, and reproducibility
• Implement ML automation and orchestration using tools like Databricks and MLflow
• Contribute to architecture discussions and drive improvements in ML platform components, feature pipelines, and experimentation processes
• Write clean, maintainable, and well-tested Python code, with attention to performance in distributed environments (e.g., PySpark)
• Support operational excellence by improving observability, failure handling, CI/CD integration, and model lifecycle management
• Participate in code reviews and support best practices for production ML development

What You'll Need:

• 2–4 years of experience in machine learning engineering, data engineering, or applied ML; or equivalent practical experience
• Strong proficiency in Python, including experience with ML/data libraries (NumPy, Pandas, scikit-learn, PySpark)
• Understanding of ML fundamentals, including training workflows, evaluation methodology, and feature engineering
• Experience with distributed computing frameworks (e.g., Spark) and large-scale data processing
• Familiarity with ML Ops pipelines and tools such as Databricks and MLflow or equivalent
• Strong problem-solving skills and the ability to take ownership of engineering components with minimal guidance
• Ability to work cross-functionally with Data Science, Software Engineering, and Product partners
• Strong communication skills and a collaborative mindset

Bonus:

• Experience deploying ML models in production environments and supporting model monitoring or retraining workflows
• Familiarity with AWS or other cloud platforms and their ML/data services
• Experience with feature stores, real-time inference pipelines, or model-serving frameworks
• Background in marketing technology, personalization systems, or large-scale consumer applications