Machine Learning Engineer III

Chewy - Boston, MA

Full-time - Mid Level

Boston, MA

Sporting Goods, Hobby, Musical Instrument, Book, and Miscellaneous Retailers

About the position

Chewy is seeking a Machine Learning Engineer III to join the Outbound Science Technology Team. This role involves leveraging machine learning, optimization modeling, simulation, data analysis, and software development to create and deploy science models that address critical challenges in fulfillment operations. The ideal candidate will have a strong background in both science models and cloud technologies, particularly in deploying and scaling machine learning and optimization models in cloud environments.

Responsibilities

• Design, develop, and implement optimization and ML models for various applications, including resource planning optimization, predictive analytics, time-series forecasting, and natural language processing.
• Research and implement innovative science-based algorithms to address specific business challenges.
• Design and implement end-to-end machine learning workflows using AWS cloud services such as Sagemaker.
• Collaborate with multi-functional teams to understand requirements and deliver effective solutions.
• Document code, algorithms, and ensure reproducibility.
• Provide technical mentorship in standard methodologies for model development and deployment to the data science team.
• Effectively communicate technical concepts and insights to both technical and non-technical customers.
• Deploy science models using established pipelines, provisioning, cloud resource management, and containerization as necessary.

Requirements

• Graduate Degree (MS or PhD or equivalent experience) in Data Science, Machine Learning, Statistics, Operations Research, or related field.
• 5+ years of experience in developing and deploying production-level systems using algorithms and machine learning models.
• Experience in optimization, forecasting, machine learning, and simulation; understanding of deep learning techniques is a plus.
• Proficiency in developing science models using Python, Java, or similar languages, as well as expertise in SQL.
• Proficiency with version control systems (e.g., Git) and coding practices.
• Strong understanding of cloud platforms for ML pipeline such as AWS Sagemaker.
• Experience with containerization & orchestration tools (e.g., Docker, Kubernetes) and Infrastructure as Code tools (e.g., Terraform, CloudFormation) is a plus.
• Strong problem-solving skills and the ability to work independently in a fast-paced environment.
• Excellent oral and written communication skills.

Nice-to-haves

• Understanding of deep learning techniques (Reinforcement Learning) is a plus.
• Experience with containerization & orchestration tools (e.g., Docker, Kubernetes) and Infrastructure as Code tools (e.g., Terraform, CloudFormation) is a plus.