Senior Machine Learning Engineer - Product Intelligence in London

Cloudshelf is transforming retail experiences by bringing the full power of online shopping into physical stores. Our API-first platform integrates with major eCommerce platforms (Shopify, Salesforce Commerce Cloud, etc) to power in-store kiosks, tablets, and touchscreen displays that help retailers extend their product ranges and drive conversion.

We are 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 will 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.

What You’ll Do

• 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.

What We’re Looking For

• 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.

Bonus points for:

• Experience working with product catalog data and taxonomies.
• Understanding of retail dynamics and shopping behaviour.
• Background in experimentation and A/B testing frameworks.
• Contributions to open source or technical writing.

Why Join Cloudshelf

• Your work directly influences purchase decisions in physical stores. You’ll see shoppers and retailers using systems you built, and you’ll have data showing the sales outcomes you’re driving.
• As one of our first specialized ML hires, you’ll define how intelligence works across our platform. This isn’t about implementing someone else’s vision - it’s about shaping the product direction.
• We’re small enough that you’ll work directly with founders and have meaningful equity, but established enough to have real customers and traction. You’re joining at the perfect inflection point.
• Work with a focused engineering team that values pragmatic solutions over resume-driven development. We ship features that matter and measure what works.
• Fully remote within UK-compatible timezones. We trust you to do great work and give you the autonomy to do it your way.

The journey ahead involves raising our seed funding, which means you’ll be part of the team that scales our platform and proves out the next generation of in-store shopping technology. The problems are interesting, the impact is measurable, and the opportunity to shape both product and team is real.

If you’re excited about applying ML to genuine retail challenges and want to build systems that merchants and shoppers actually use, let’s talk.