Senior Machine Learning Engineer, Cloudshelf

• 3+ years of experience applying machine learning to real-world product problems
• Strong backend engineering skills, particularly in TypeScript
• Deep SQL expertise - you're comfortable writing complex queries and working with large datasets
• Experience building and deploying recommendation systems, ideally in eCommerce or retail contexts
• Track record of owning features end-to-end: from concept through production deployment and optimization
• Pragmatic approach to ML - you know when to use sophisticated techniques and when simpler solutions win
• Confidence to push back on use of data sets we shouldn't be "learning" from
• Excellent communication skills - you can discuss algorithms and approaches with non-technical stakeholders

Desirable

• We're looking for a Senior Machine Learning Engineer who can design, build, and optimize the intelligent systems that power our product recommendations, buyer guides, and upsell/cross-sell features
• This isn't about building models from scratch - it's about cleverly leveraging existing ML capabilities (including LLMs) and creating the feedback loops that make them better over time
• You'll work directly with our CEO (Head of Product) and report to our CTO, with a clear path to a staff-level role as we scale. This is a unique opportunity to own the intelligence layer of a platform that's changing how retail works on a global level
• Architect recommendation systems that identify product complementarity and drive upsell opportunities across diverse product catalogs
• Design scoring algorithms and business logic for buyer guides that help customers make confident purchase decisions
• Define the metrics and feedback loops that continuously improve recommendation quality
• Implement production-grade features that integrate seamlessly with our API-first architecture
• Build data pipelines that process product catalogs from multiple eCommerce platforms
• Write clean, maintainable, tested TypeScript backend code that other engineers can work with
• Optimize for performance and scale as we grow our retail footprint
• Instrument tracking systems to understand how recommendations perform in real retail environments
• Run experiments to validate algorithm improvements and feature variations
• Use SQL to analyze product data, user behavior, and conversion patterns
• Turn insights into actionable product improvement