Currently has or is in the process of obtaining a PhD degree in Computer Science, Electrical Engineering, Computer Vision, Machine Learning, Artificial Intelligence, relevant technical field, or equivalent Research and/or work experience in video generation, computer vision, or multimodal AI Experience with large-scale model training, post-training optimization techniques, and/or data curation Experience in Python, C++, or other related languages, and deep learning frameworks (e.g., PyTorch, TensorFlow) Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Demonstrated research and software engineering experience via internships, industry or academic work experience, coding competitions, or widely used contributions in open source repositories (e.g., GitHub) Experience with video generation, video synthesis, or multimodal fusion techniques Experience with video-language models and architectures relevant to video generation and post-training Experience with LLM/VLM agents, reward modeling, or evaluation of generative models Proven track record of achieving significant results, as demonstrated by grants, fellowships, patents, or publications at leading workshops, journals, or conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV) Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources Experience solving complex problems and evaluating alternative solutions, tradeoffs, and perspectives to determine a path forward Experience working and communicating cross-functionally in a collaborative, interdisciplinary team environment Intent to return to degree-program after the completion of the internship/co-opMeta builds technologies that help people connect, find communities, and grow businesses. We're especially interested in candidates with expertise across video generation, computer vision, and multimodal AI, including: Generative modeling for video (diffusion, flow matching, video synthesis) Post-training of large-scale video and multimodal models (SFT/RL, optimization, data curation) Multimodal understanding, reasoning, and generation Self-improving and agentic systems (multi-agent LLM/VLM systems, tool use, planning) Reward modeling and LLM/VLM-as-a-judge.