Principal ML Scientist – Ranking & Retrieval

TripAdvisor LLC; Greater London; Hybrid; GBP 140,000 - 190,000; Full time

Benefits offered by this job

Base salary + annual bonuses

Remote-friendly with on-site option

Tuition assistance

Travel perks

Health benefits

Generous referral scheme

Job summary

Tripadvisor LLC in London, United Kingdom is seeking a Principal Machine Learning Scientist to lead the core discovery engine for ranking, retrieval, and recommendations. You will define the ML strategy, build scalable systems, and mentor a high-performing team across machine learning, MLOps, and experimentation.

You will drive multi-task models, graph-aware representations, and agentic AI approaches to improve user search, discovery, and itineraries at scale within the Trips vertical.

Qualifications

• PhD or Master’s in CS/ML or quantitative field.
• 8+ years of industry experience deploying large-scale ML models.
• Experience shipping production ML systems for millions of users.

Responsibilities

• Lead technical roadmap for Search, Retrieval, Ranking and Recommendation.
• Design scalable ML architectures for multi-objective optimization.
• Oversee low-latency, high-throughput pipelines and vector search.
• Mentor senior and mid-level ML scientists; promote MLOps and AB/testing.

Skills

PhD or MSc in CS/ML

8+ years ML experience

MTL (Multi-Task Learning)

MMoE

Sequential modeling

Representation learning

Python (TF, PyTorch)

Distributed computing (Spark, Ray)

Cloud: AWS/GCP

Graph Neural Networks

Agentic AI / LLMs

E-commerce / Travel tech

Publications / OSS

Education

PhD or Master’s in Computer Science or quantitative field

Tools

TensorFlow

PyTorch

Spark

Ray

AWS

GCP

Job description

Tripadvisor LLC in London, United Kingdom is seeking a Principal Machine Learning Scientist to lead the core discovery engine for ranking, retrieval, and recommendations. You will define the ML strategy, build scalable systems, and mentor a high-performing team across machine learning, MLOps, and experimentation.

You will drive multi-task models, graph-aware representations, and agentic AI approaches to improve user search, discovery, and itineraries at scale within the Trips vertical.