Chewy - Bellevue, WA
posted 3 months ago
Chewy is seeking a Machine Learning Engineer III to join our Merchandising Data Science Team. In this pivotal role, you will leverage your expertise in machine learning, advanced data analysis, statistical testing, and software development to design, implement, and deploy machine learning models that address complex challenges faced by our Retail Operations business partners. The ideal candidate will function as a full stack data scientist, possessing a strong foundation in both machine learning and cloud technologies, with a particular emphasis on deploying and scaling machine learning models within cloud environments. This position offers the opportunity to create and develop backend ML frameworks tailored to various business problems and to construct engineering pipelines that facilitate efficient model deployment. As a Machine Learning Engineer III, your responsibilities will include designing, developing, and implementing machine learning models for a range of applications, such as predictive analytics, natural language processing, and computer vision. You will be tasked with researching and applying cutting-edge machine learning algorithms to solve specific business challenges. Additionally, you will design and implement cloud architectures that support end-to-end machine learning workflows, ensuring that they are scalable, reliable, and performant. Your role will also involve utilizing Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation to automate the provisioning and management of cloud resources necessary for machine learning. You will implement and manage containerization solutions, such as Docker, and orchestration tools like Kubernetes to deploy and scale machine learning applications effectively. Collaboration with cross-functional teams, including data scientists, software engineers, and domain experts, will be essential to understand requirements and deliver impactful solutions. Documentation of code, algorithms, and processes will be crucial for knowledge sharing and reproducibility. Furthermore, you will provide technical guidance on best practices for model development and deployment to the data science team, while also communicating complex technical concepts and insights to both technical and non-technical stakeholders.