
5001 - 10000 employees
🛒 Retail
🛍️ eCommerce
👥 B2C
Retail • eCommerce • B2C
Crate and Barrel is an industry-leading home furnishings specialty retailer founded in 1962 that helps people build purposeful homes across a family of brands including Crate & Barrel, Crate & Kids, CB2, and Hudson Grace. A leader in omnichannel and direct-marketing retail, the company operates more than 100 stores in the U. S. and Canada, supports international franchise locations and eCommerce platforms in multiple countries, and offers curated sourcing, production, custom delivery and set-up in over 90 countries. Owned by the Otto Group, Crate & Barrel emphasizes the physical retail experience alongside robust digital and eCommerce capabilities and employs roughly 7,500 associates.
🕒 August 24
Apache
BigQuery
Cloud
Docker
Google Cloud Platform
Kubernetes
Pandas
Python
PyTorch
Scikit-Learn
Spark
SQL
Tensorflow
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5001 - 10000 employees
🛒 Retail
🛍️ eCommerce
👥 B2C
Retail • eCommerce • B2C
Crate and Barrel is an industry-leading home furnishings specialty retailer founded in 1962 that helps people build purposeful homes across a family of brands including Crate & Barrel, Crate & Kids, CB2, and Hudson Grace. A leader in omnichannel and direct-marketing retail, the company operates more than 100 stores in the U. S. and Canada, supports international franchise locations and eCommerce platforms in multiple countries, and offers curated sourcing, production, custom delivery and set-up in over 90 countries. Owned by the Otto Group, Crate & Barrel emphasizes the physical retail experience alongside robust digital and eCommerce capabilities and employs roughly 7,500 associates.
• Design, develop, train, and fine-tune complex machine learning models using deep learning and classical techniques • Deploy models primarily on Google Cloud Platform • Own end-to-end model deployment on Vertex AI, GKE, and Cloud Run • Build and maintain MLOps pipelines for automated training, testing, versioning, and CI/CD • Design and build agentic AI systems and multi-agent workflows • Write clean, tested, production-grade Python and C# code • Participate in code reviews, team ceremonies, sprint planning, and continuous process improvement • Profile and optimize training and inference speed and cost • Author technical user stories covering the ML development lifecycle • Partner with Data Engineering on data infrastructure, features, and pipelines • Partner with DevOps and Cloud teams on reliable, cost-optimized ML solutions • Collaborate with product owners and stakeholders on technical solutions and roadmaps • Implement monitoring dashboards for model drift, accuracy, latency, and cost • Identify, develop, and validate features to improve model performance and generalization • Mentor engineers and provide technical leadership on architecture decisions
• Strong hands-on experience with Google Cloud Platform for ML, including Vertex AI, BigQuery, Cloud Run, GKE, and Cloud Build • Strong proficiency in Python and C# • Deep experience with TensorFlow, PyTorch, and scikit-learn • Knowledge of Google ADK, AutoGen, LangChain, LlamaIndex, and Gemini/Vertex AI foundation models • Understanding of distributed training and model serving architecture • Experience with MLOps tools including Vertex AI Pipelines, MLflow, DVC, and Kubeflow • Experience with Docker and Kubernetes/GKE • Direct experience deploying ML solutions on Google Cloud; Vertex AI required • Solid foundation in machine learning, statistics, and optimization • Proficiency in SQL, BigQuery, Pandas, Dataflow/Apache Beam, and Spark • Understanding of agile methodologies • Strong communication, collaboration, and technical leadership skills • Ability to mentor and guide engineers • Strong software engineering fundamentals, including coding standards, code reviews, source control, testing, and operations • Bachelor's degree in Computer Science, Software Engineering, Information Systems, or related field, or equivalent practical experience • 5+ years of experience in machine learning engineering • Proven track record deploying at least 2–3 significant ML models into high-availability production systems • Must be authorized to work in the United States
• Reasonable accommodations for individuals with disabilities during the application process and performance of essential job functions • Equal opportunity employment
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