Senior Principal Machine Learning Engineer - Recommendations

This Principal Machine Learning Scientist role at Tripadvisor leads the ML strategy for the core discovery engine powering travel itinerary search and recommendations. The scientist will design and scale next-generation ranking and recommendation models using multi-task learning, sequential modeling, and graph-based approaches, while mentoring a team of scientists. The role requires 10+ years of industry experience, a PhD/Master's in a quantitative field, and mastery of Python with deep learning frameworks.

This role develops and deploys machine learning models for recommendation systems, improving user engagement and personalization. The engineer works on feature engineering, model training, and online serving, requiring strong ML and distributed systems expertise.

Develops and optimizes recommendation systems to personalize customer experiences across Nubank's platform. Focuses on building scalable ML models, improving relevance, and driving engagement through data-driven insights.

This Senior Machine Learning Engineer role focuses on developing Pinterest Canvas, an internal text-to-image generative model, within the Pinterest Labs organization. The role involves prototyping new model architectures, working with large-scale visual-text datasets, conducting RLHF, and publishing research, requiring hands-on experience with diffusion models and computer vision.

This role leads the internal communications strategy, managing messaging, change communications, and employee engagement initiatives across the organization. It requires strong leadership and communication skills.

This role develops and maintains full-stack features to support artist success on the platform, working on both frontend and backend systems. It requires collaboration with cross-functional teams to enhance the artist experience.

This role focuses on developing and optimizing machine learning models for SoundCloud's recommendation systems, enhancing user experience through personalized content. The engineer will work on large-scale data pipelines and model deployment, collaborating with cross-functional teams to improve discovery and engagement.