Research Scientist Intern, Generative Recommendation (PhD)

As a Research Intern, you will collaborate with researchers and engineers to explore new approaches to personalized retrieval, ranking, and recommendation list generation. This opportunity is especially suited to researchers with backgrounds in LLMs and computer vision who want to apply diffusion models and reinforcement learning to recommendation problems.

Responsibilities

Conduct research on generative recommendation, including autoregressive transformers, diffusion models, and flow matching for personalized retrieval and ranking. Explore multimodal modeling approaches that combine images, video, text, and user interaction histories to learn content representations and user preferences. Develop reinforcement learning, reward modeling, and preference optimization methods to improve recommendation quality and long-term user satisfaction. Design, implement, and evaluate novel model architectures and training algorithms through rigorous experimentation. Collaborate closely with researchers and engineers to translate research ideas into scalable recommendation methods. Contribute to publications at leading AI and machine learning conferences, and present findings that help shape ongoing research and future directions.

Qualifications

Has, or is in the process of obtaining, a Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, generative modeling, computer vision, or reinforcement learning Programming experience in Python and hands-on experience with deep learning frameworks such as PyTorch Experience with LLMs and computer vision, including familiarity with diffusion models and reinforcement learning Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment Intent to return to the degree program after completing the internship Demonstrated research achievements through publications at leading conferences such as NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, KDD, or RecSys Hands-on research experience developing diffusion models, flow matching methods, multimodal foundation models, or reinforcement learning algorithms Experience with reward modeling, offline reinforcement learning, preference optimization, or post-training of generative models Research experience in recommendation systems, personalization, or sequential modeling Contributions to open-source projects, benchmarks, or reproducible research artifacts Ability to identify research opportunities in practical applications, analyze experimental results, and independently drive research iterations while collaborating across teams