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 (and your birthday off!) 🌴
• Pension contributions 🛡️
• Private health 🏥
• Gym and wellness benefits 💪
• Mental health benefits 🧠
• Quarterly team offsite budget 🎉
• Thursday happy hour 🍻
• 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.