Sr Applied ML Scientist

Job highlights

• Proven experience building retrieval and ranking pipelines. . Strong offline evaluation methodology and rigor. . Proficiency in Python and modern ML frameworks.

• Demonstrated research-to-production experience,not research-only. . Likely scope includes traveller,property,and event embeddings

• Experience with sparse implicit-feedback modelling and cold-start problem-solving.

• Design and build embeddings and representation learning models for traveller, property, and event data.

• Develop two-tower retrieval architectures for recommendation and matching use cases.

• Build models for sparse implicit-feedback scenarios and address cold-start challenges.

• Design social/graph-based recommendation approaches for traveller matching and similarity.

• Build retrieval and ranking pipelines, with rigorous offline evaluation practices.

• Take research-backed approaches from prototype through to production deployment.

• Integrate embeddings and recommendation outputs into products such as Wayfinder/recommendations.

• Strong experience with embeddings and representation learning.

• Hands-on experience with two-tower retrieval architectures.

• Experience with sparse implicit-feedback modelling and cold-start problem-solving.

• Experience with social/graph-based recommendation systems.

• Proven experience building retrieval and ranking pipelines.

• Strong offline evaluation methodology and rigor.

• Proficiency in Python and modern ML frameworks.

• Demonstrated research-to-production experience, not research-only.

• Likely scope includes traveller, property, and event embeddings; social similarity; event relevance; and integration into recommendation systems.

• Avoid: generic GenAI/NLP profiles without recommendation systems experience.

• Screening question: "Given stays, co-location, profile views, chats, Linkups and event attendance, how would you build and evaluate traveller embeddings"

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