Swap is the infrastructure behind modern agentic commerce. The only AI-native platform connecting backend operations with a forward-thinking storefront experience.
Built for brands that want to sell anything - anywhere, Swap centralizes global operations, powers intelligent workflows, and unlocks margin-protecting decisions with real-time data and capability. Our products span cross-border, tax, returns, demand planning, and our next-generation agentic storefront, giving merchants full transparency and the ability to act with confidence.
At Swap, we’re building a culture that values clarity, creativity, and shared ownership as we redefine how global commerce works.
About the Role
As Senior/Lead ML Engineer (Recommendations), you will own the intelligence behind what Swap's AI Storefront shows to every shopper. This is a deeply technical, hands-on role at the intersection of recommendation systems, LLMs, and fashion understanding. You'll build the models and pipelines that power style-aware product recommendations, outfit generation, and personalised discovery, working end-to-end from research and prototyping through to production systems serving real customers. You'll work closely with our conversational AI layer, which extracts rich preference signals through dialogue, and find ways to combine that with traditional e-commerce behavioural data and LLM-based world knowledge to bootstrap and refine recommendations, including solving cold-start problems in novel ways.
You'll set a high technical bar for ML engineering within the recommendations space at Swap, and as we scale, you'll play a key role in how this area of the team evolves.
Key Responsibilities
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.
What We Would Like to See
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
Diversity & Equal Opportunities
We embrace diversity and equality in a serious way. We are committed to building a team with a variety of backgrounds, skills, and views. The more inclusive we are, the better our work will be. Creating a culture of equality isn't just the right thing to do; it's also the smart thing.
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