Lead ML Engineer – Recommendation Systems

🕒 April 7

🏢🏡 London – Hybrid

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Swap

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2022

🛍️ eCommerce

☁️ SaaS

🤖 Artificial Intelligence

eCommerce • SaaS • Artificial Intelligence

Swap is an AI-powered commerce platform that replaces static websites with an "agentic storefront" — an AI-led, conversational shopping experience that guides customers from discovery through virtual try-on to checkout. The company also provides back-office commerce operations tools for global brands, including cross-border pricing, tax and duty calculation, returns automation, inventory demand signals, and Shopify/API integrations to streamline fulfillment and compliance. Swap is positioned to help e-commerce and retail brands increase conversions, reduce returns, and simplify international operations.

📋 Description

• Own the end-to-end ML lifecycle for recommendation and personalisation systems, from problem framing and data exploration through to deployment, evaluation, and iteration. • Design, build, and productionise models for style-aware recommendations, including item pairing, outfit generation, preference matching, and personalised discovery. • Develop approaches that combine conversational preference extraction (from our memory layer) with traditional behavioural signals and LLM-based world knowledge to power high-quality recommendations, particularly in cold-start and sparse-data scenarios. • Build and optimise the feature pipelines and serving infrastructure that power recommendations at scale, working closely with engineering. • Define and champion best practices for offline and online evaluation of recommendation quality, including metrics for relevance, diversity, novelty, and style coherence. • Collaborate closely with product, AI engineering, and design to shape how recommendations surface across the AI Storefront, from conversational flows to visual discovery experiences. • Explore and integrate signals from social media content and visual style to enrich user taste profiles and improve recommendation relevance. • Act as a senior technical reference point for recommendation and personalisation engineering at Swap, helping to set standards, review critical work, and guide teammates.

🎯 Requirements

• Significant experience (typically 5+ years) in ML engineering or applied machine learning roles, with clear ownership of production recommendation or personalisation systems that drove meaningful business outcomes. • Strong hands-on skills in Python and relevant ML/deep learning frameworks (e.g. PyTorch, TensorFlow), plus solid software engineering practices (testing, version control, code review, CI/CD). • Proven track record building recommendation systems, with practical experience in techniques such as collaborative filtering, content-based methods, embedding models, sequence models, or graph-based approaches. • Experience with LLMs and a practical understanding of how to leverage them within recommendation pipelines, whether for feature enrichment, preference understanding, knowledge bootstrapping, or hybrid retrieval approaches. • Comfort working with fashion, style, or visual domains is a strong plus, particularly experience with visual embeddings, multimodal models, or taste/preference modelling. • Practical experience deploying and iterating on ML systems in production (model serving, monitoring, retraining strategies, working with APIs and microservices).

🏖️ Benefits

• Competitive base salary • Stock options in a high-growth startup • Competitive PTO with public holidays additional • Private health • Pension • Wellness benefits • Breakfast Mondays