Staff/Principal Machine Learning Scientist – Ranking & Retrieval

Company: TripAdvisor
Apply for the Staff/Principal Machine Learning Scientist – Ranking & Retrieval
Location: London
Job Description:

Overview

As Principal Machine Learning Scientist at Tripadvisor, you will anchor the core discovery engine and steer ML strategy across search, retrieval, ranking, and recommendations. You’ll bridge cutting-edge AI research with production systems to influence user engagement and bookings at scale. You’ll tackle multi-task ranking, sequential modeling, and graph-based travel recommendations, mentoring a team of scientists and shaping long-term ML architecture. This role offers scope to impact millions of travelers and advance the Trips ecosystem through state-of-the-art AI.

Pay / Benefits

  • Competitive compensation
  • Remote-friendly with flexible on-site options
  • Flexible schedule
  • Tuition assistance
  • Travel perks
  • Health benefits

Responsibilities

  • Set the technical roadmap for search, retrieval, ranking, and recommendations within the Trips vertical
  • Design and scale next-generation ML models for multi-objective optimization
  • Oversee low-latency, high-throughput data pipelines and real-time retrieval/ranking (e.g., vector search)
  • Collaborate with Product, Engineering leads, and DS peers to align ML initiatives with business goals
  • Mentor and develop senior and mid-level ML scientists, promoting MLOps, A/B testing, and data privacy

Key requirements

  • 8+ years of industry experience shipping large-scale ML models in production
  • Experience deploying systems with millions of active users
  • Deep knowledge of SOTA retrieval and ranking, MTL, and MMoe or similar architectures
  • Strong background in sequential/temporal modeling and representation learning
  • Proficiency with Python and DL frameworks (TensorFlow, PyTorch)
  • Experience with distributed computing (Spark, Ray) and cloud platforms (AWS/GCP)
  • Graduate-to-executive communication
  • Mentorship and team leadership
  • Cross-functional collaboration
  • TensorFlow
  • PyTorch
  • Spark

Posted: September 14th, 2026