Research Scientist Graduate (ML Recommendation Systems) - 2026 Start (PhD)

Responsibilities

• Drive the next wave of innovation for our recommendation systems, directly shaping the user experience by:
• Building and scaling up machine learning models for recommendation systems
• Researching and applying multi-modal techniques (leveraging text, image, video) to create a holistic understanding of content and user preferences
• Pioneering new modeling strategies by researching and integrating long-term user behavior signals to drive sustained engagement and satisfaction, by using techniques such as reinforcement learning
• Partnering closely with the infrastructure team to co-design and optimize next-generation recommendation model architectures and systems, ensuring high-performance, low-latency, and cost-efficient training and inference at a massive scale
• Working hand-in-hand with product, engineering, and design teams to rigorously test and deploy end-to-end solutions, validating their impact and ensuring they create a seamless and enhanced user experience

Qualifications

• Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• At least 5 years of experience in proficiency in one or more programming languages such as Python or C++, and deep learning frameworks like PyTorch or TensorFlow.
• Demonstrated expertise in designing, building, and scaling machine learning models for recommendation systems.
• Deep understanding and hands-on experience with modern deep learning techniques, including Transformers, Large Language Models (LLMs), and multi-modal learning.
• Proven experience in building and deploying end-to-end ML pipelines in a production environment.