Qualifications

• Bachelor's degree in Computer Science, Machine Learning, or a related field
• 2+ years of experience as a Machine Learning Engineer or in a similar role
• Strong programming skills in Python or another relevant language
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn)
• Experience with SQL and relational databases
• Familiarity with big data technologies (e.g., Spark, Iceberg, dbt)
• Familiarity with AWS cloud technologies (e.g., Glue, Sagemaker, Athena, Redshift)
• Experience with data visualization tools (e.g., Tableau, Streamlit, Plotly)
• Knowledge of retail and e-commerce
• Solid understanding of machine learning algorithms and best practices
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP)
• Excellent communication and presentation skills
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Responsibilities

• In this role, you will play a crucial part in building and deploying machine learning solutions that empower our Chewy Customer Care teams
• As a Machine Learning Engineer II, you will be a key contributor throughout the machine learning lifecycle, from data preparation and model development to deployment and monitoring
• You will have the opportunity to work on diverse projects and contribute directly to the success of the company
• Design and implement robust data pipelines for efficient data ingestion, processing, and feature engineering
• Ensure data quality and consistency for model training and inference
• Work with large datasets and apply data scaling techniques
• Select and build appropriate machine learning models based on business requirements and data characteristics
• Train and evaluate models using various techniques and metrics
• Fine-tune models to achieve optimal performance and efficiency
• Deploy machine learning models to production environments, ensuring scalability, reliability, and maintainability
• Implement monitoring and alerting systems to track model performance and identify potential issues
• Optimize models for inference speed and resource utilization
• Work closely with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions
• Effectively communicate technical concepts and findings to both technical and non-technical audiences