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Lead Machine Learning Engineer - Recommendation Systems

SwapLondon
On-site Full-time

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Experience Level

Manager

Qualifications

Key ResponsibilitiesOversee the complete ML lifecycle for recommendation and personalization systems, from problem definition and data exploration to deployment, evaluation, and iterative improvement. Design, construct, and implement models for style-aware recommendations, including item pairing, outfit generation, preference matching, and personalized discovery. Develop strategies that integrate conversational preference extraction with traditional behavioral signals and LLM-based world knowledge to generate high-quality recommendations, especially in cold-start and sparse-data environments. Build and optimize feature pipelines and serving infrastructures to enable scalable recommendations, closely collaborating with engineering teams. Establish and advocate for best practices in offline analysis and deployment processes.

About the job

About Swap

Swap serves as the backbone of contemporary agentic commerce, being the only AI-native platform that seamlessly integrates backend operations with an innovative storefront experience.

Designed for brands looking to sell anything, anywhere, Swap centralizes global operations, enhances intelligent workflows, and empowers margin-protecting decisions through real-time data and capabilities. Our expansive product range covers cross-border transactions, tax solutions, returns, demand planning, and our state-of-the-art agentic storefront, providing merchants with complete transparency to act confidently.

At Swap, we cultivate a culture that prioritizes clarity, creativity, and shared ownership as we transform the landscape of global commerce.

About the Role

As the Lead Machine Learning Engineer focusing on Recommendations, you will be responsible for shaping the intelligence behind the AI Storefront at Swap, determining what products are displayed to each shopper. This position is highly technical and hands-on, merging expertise in recommendation systems, large language models (LLMs), and fashion understanding. You will develop the models and pipelines that drive style-aware product recommendations, outfit generation, and personalized discovery, managing the entire process from research and prototyping to production systems that serve actual customers. Collaboration with our conversational AI layer will be essential, as you will extract insightful preference signals through dialogue and integrate these with traditional e-commerce behavioral data and LLM-based world knowledge to enhance recommendations, particularly addressing cold-start challenges in innovative ways.

You will establish high technical standards for ML engineering in the recommendations domain at Swap, playing a pivotal role in the evolution of this team area as we scale.

About Swap

Swap is at the forefront of AI-driven commerce, providing brands with tools to efficiently manage global operations and enhance the shopping experience. Our innovative platform combines advanced technology with a commitment to transparency and effective decision-making, redefining how brands engage in global commerce.

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