Wayfair is seeking an ML Engineer to develop and deploy robust, reliable, and production-grade ML and AI systems to transform their Supply Chain and Support operations, focusing on scalability, reliability, and integration with partner systems.
Requirements
- Strong programming skills in Python and experience with production-quality codebases
- Experience with cloud platforms (preferably GCP), containerization (Docker), orchestration (Kubernetes), and CI/CD workflows.
- Familiarity with monitoring and observability tools (e.g., Datadog).
- Proven experience deploying and integrating ML/AI models into production systems
- Familiarity with MLOps and AIOps practices and tools (e.g. data pipeline, model orchestration, feature store)
- Experience building RAG and agentic AI systems
Responsibilities
- Build and own ML production systems for both real-time and batch inference, ensuring scalability, reliability, observability, and low latency.
- Lead ML service integration with partner systems by establishing ownership boundaries, API contracts, clear SLAs, and fallback mechanisms
- Architect and build complex multi-modal and Agentic AI systems by leveraging the latest advancement in LLM
- Collaborate with ML Scientists to build reusable and scalable production-grade training code and pipelines using modern MLOps practices and infrastructure (GCP and open source)
- Deploying and integrating ML/AI models into production systems
- Building RAG and agentic AI systems
Other
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field or equivalent practical experience
- 5+ years of relevant industry experience, preferably including previous experience in ML infrastructure or AI/ML engineering
- Able to work autonomously in cross-functional teams, communicate clearly with ML scientists and software engineers, and balance engineering quality with business impact. Comfortable leading technical discussions and driving alignment without formal authority.
- Candidates for this position are to be based, or plan to relocate to Boston, and will be expected to comply with their team's hybrid work schedule requirements. Our teams are onsite in our Boston office Tuesday-Thursday, and remote Monday and Friday.
