Software Engineer III - Generative AI Technology

Job Description

Candidates for this position can be based in Boston, Massachusetts; or Toronto; Ontario, Canada. This is primarily a hybrid role that requires 3 times a week in office, in addition to scheduled team collaboration required at the Wayfair Office in Boston, Massachusetts, up to 4 times per year. The required team core hours are 9am-5pm EST.

Who We Are Wayfair’s Visual AI Technology team is pioneering the future of home visualization. We leverage advanced generative AI workflows, internal automated content pipelines, and partner with our creative teams to deliver immersive, dynamic, and scalable visual experiences for millions of customers.

As a Software Engineer III on this team, you will build and maintain the core systems, APIs, and ML infrastructure that power our generative visuals. Operating at the intersection of machine learning, platform scalability, and full-stack development, you will develop the frameworks and self-service environments that streamline our automated content pipelines and empower our digital artists.

What You’ll Do

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System Ownership: Be responsible for making technical decisions related to multiple components of an existing code base / system that is managed by your team / atomic teams.

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Autonomous Execution: While you will be assigned work by your Team Lead (EM/L4\+ ICs), you will also make autonomous decisions about prioritizing your work.

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Ambiguous Problem Solving: Solve problems that arise within the components of an existing code base / system managed by your atomic team. You will tackle problems that are more ambiguous because they are scoped (but not defined) for you.

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Pattern Recognition \& Improvement: Identify code or design patterns across an existing codebase / system. Ensure the outcome of your work improves multiple components of the codebase / system.

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Cross-Functional Collaboration: Interface with members of adjacent atomic teams within the same pod (eg: PM, XD, Analytics) to align on deliverables.

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Drive Measurable Impact: Have a measurable business impact downstream on team members and the codebase within your atomic team, and upstream within your pod.

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Build \& Deploy: Design scalable and resilient microservices that interface with external APIs and internal models, containerizing applications using Docker and managing services in Kubernetes (pods, services, ConfigMaps, secrets).

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Actively utilize AI-powered developer tools (e.g., Cursor, Co-Pilot, Google AI Suite) to enhance coding productivity and efficiency.

We Are a Match Because You Have:

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A solid understanding of Machine Learning fundamentals (e.g., supervised vs. unsupervised learning, model evaluation metrics) and experience with common ML libraries/frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).

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Familiarity with fine-tuning or serving large models (e.g., LLMs, diffusion models) and experience working with APIs for ML services (e.g., OpenAI, RunwayML, HuggingFace, Fal.ai).

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Strong Python skills (experience with FastAPI or Flask is a bonus) and a solid understanding of cloud platforms (GCP/AWS/Azure) for compute-heavy workloads.

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Comfort consuming and exposing REST and/or GraphQL APIs, along with experience using authentication methods (OAuth, API keys, JWT).

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Familiarity with writing unit and integration tests, CI/CD tools (e.g., Buildkite, GitHub Actions, Jenkins), and monitoring production systems (observability: logs, metrics, tracing).

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An understanding of data preprocessing pipelines for ML input/output, and experience working with both structured and unstructured data (images, text, video).

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Familiarity with data storage solutions like GCS, PostgreSQL, BigQuery, or MongoDB.

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Proficiency with React, building interactive UIs, and frontend state management (e.g., Redux, Zustand).

Nice to Have:

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Experience with model optimization or quantization for deployment.

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Understanding of model lifecycle management (MLflow, Weights \& Biases).

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Some DevOps experience (Terraform, Helm, ArgoCD).

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Experience with GPU-enabled workloads and scheduling in Kubernetes.

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Previous experience in a 3D content pipeline (e.g., architectural visualization, games, VFX workflows).

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Experience building interactive UIs using React and modern frontend practices for ML-powered tools, integrating securely with backend APIs and authentication layers.