Skills

ab-testing
ecommerce
embeddings
llm
machine-learning
recommendation-systems
vector-search

Must have

2-7 years of experience working on personalisation, recommendations, search, or ranking at scale.
Prior experience in a B2C product - social, ecommerce, fashion, dating, gaming, or video platforms.
Exposure to a wide range of popular recommendation and personalisation techniques, including collaborative filtering, deep retrieval models (e. g., two-tower), learning-to-rank, embeddings with ANN se
Exposure to training models OR deploying them - experience with end-to-end ML pipelines.
Understand offline and online evaluation, A/B testing, and metric alignment.
Experience with vector search, graph-based algorithms and LLM-based approaches is a big plus.

Full description

You are a full-stack ML data scientist-engineer who can design, model, and deploy recommendation systems and ideally has led initiatives in recsys, feed ranking, or search.

Requirements:

2-7 years of experience working on personalisation, recommendations, search, or ranking at scale.
Prior experience in a B2C product - social, ecommerce, fashion, dating, gaming, or video platforms.
Exposure to a wide range of popular recommendation and personalisation techniques, including collaborative filtering, deep retrieval models (e. g., two-tower), learning-to-rank, embeddings with ANN search, and LLM approaches for sparse data personalisation.
Exposure to training models OR deploying them - experience with end-to-end ML pipelines.
Understand offline and online evaluation, A/B testing, and metric alignment.
Experience with vector search, graph-based algorithms and LLM-based approaches is a big plus.