Machine Learning Engineer III

Chewy - Minneapolis, MN

Full-time - Mid Level

Minneapolis, MN

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

About the position

Chewy is looking for a Machine Learning Engineer III to join our Merchandising Data Science Team. In this role, you will combine an understanding of machine learning, advanced data analysis, statistical testing, and software development. As a Machine Learning Engineer III, you will play a crucial part in designing, implementing, and deploying machine learning models to solve complex problems for our Retail Operations business partners. The ideal candidate will operate as a full stack data scientist who should have expertise in both machine learning and cloud technologies, with a focus on deploying and scaling machine learning models in cloud environments. In this role, they will have the opportunity to design and develop backend ML frameworks best suited for different business problems and build engineering pipelines to streamline model deployment. In this position, you will be responsible for designing, developing, and implementing machine learning models for various applications, including but not limited to predictive analytics, natural language processing, and computer vision. You will research and implement state-of-the-art machine learning algorithms to address specific business challenges. Additionally, you will design and implement cloud architectures tailored for end-to-end machine learning workflows, ensuring scalability, reliability, and performance. Utilizing Infrastructure as Code (IaC) tools, such as Terraform or AWS CloudFormation, you will automate the provisioning and management of cloud resources for machine learning. You will also implement and manage containerization solutions (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and scaling machine learning applications. Collaboration with cross-functional teams, including data scientists, software engineers, and domain experts, will be essential to understand requirements and deliver effective solutions. Documenting code, algorithms, and processes will facilitate knowledge sharing and ensure reproducibility. Furthermore, you will provide technical guidance in best practices for model development and deployment to the data science team, and effectively communicate complex technical concepts and insights to both technical and non-technical stakeholders.

Responsibilities

• Design, develop, and implement machine learning models for various applications, including predictive analytics, natural language processing, and computer vision.
• Research and implement state-of-the-art machine learning algorithms to address specific business challenges.
• Design and implement cloud architectures tailored for end-to-end machine learning workflows, ensuring scalability, reliability, and performance.
• Utilize Infrastructure as Code (IaC) tools, such as Terraform or AWS CloudFormation, to automate the provisioning and management of cloud resources for machine learning.
• Implement and manage containerization solutions (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and scaling machine learning applications.
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements and deliver effective solutions.
• Document code, algorithms, and processes to facilitate knowledge sharing and ensure reproducibility.
• Provide technical guidance in best practices for model development and deployment to the data science team.
• Effectively communicate complex technical concepts and insights to both technical and non-technical stakeholders.

Requirements

• Graduate Degree (MS/PhD) in Data Science, Machine Learning, Statistics, Mathematics, or related discipline.
• 7+ years of experience in developing and deploying advanced machine learning models and algorithms in a production environment.
• Strong understanding of statistical analysis, machine learning, and deep learning techniques.
• Expertise in programming languages such as Python.
• Advanced proficiency with SQL.
• Strong understanding of cloud platforms such as AWS.
• Proficiency in Infrastructure as Code tools (e.g., Terraform, CloudFormation).
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Proficiency with version control systems (e.g., Git) and collaborative coding practices.
• Strong problem-solving skills and the ability to work independently and collaboratively in a fast-paced environment.
• Excellent oral and written communication skills including the ability to communicate effectively with both technical and non-technical stakeholders.