Machine Learning Scientist - MM

Are you excited by search, ranking and recommendation systems at scale?

Do you enjoy turning complex product challenges into elegant, production-ready ML architecture?

Our client is a major digital platform hiring a Principal Machine Learning Scientist to shape the technical direction for intelligent discovery, planning and personalisation across a large-scale marketplace environment.

This is a hands-on technical leadership role, not a people management position. You will define architecture, raise engineering and ML standards, coach strong technical teams and help turn an early product vision into scalable production systems.

Why This Role is Great

You will have the chance to:

• Shape the ML vision for search, discovery and planning products.
• Design advanced retrieval, ranking and recommendation systems.
• Work with sequential modelling, embeddings, semantic retrieval and multimodal learning.
• Influence MLOps, experimentation, system design and production quality.
• Partner closely with Product, Data and Engineering leaders.
• Coach and mentor other ML specialists while staying deeply technical.

About You

You will likely bring strong experience in:

• Machine learning for search, ranking, recommendations or personalisation.
• Multi-objective optimisation, such as MTL, MMoE or similar approaches.
• Sequential recommendation systems, temporal modelling or session-based modelling.
• Representation learning, embeddings, semantic retrieval or multimodal ML.
• Python and frameworks such as PyTorch or TensorFlow.
• Cloud or distributed systems such as AWS, GCP, Spark or Ray.

A PhD is not essential. Our client is more interested in deep applied ML expertise, production experience and senior technical influence. Around 7+ years' relevant industry experience would be a useful guide.

Location

This role is based in London , with hybrid working and around one day per week in the office .

Our client and our recruitment team are committed to inclusive hiring and welcome applications from people with transferable experience, even where every requirement is not met.

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