Research Scientist Intern, VLM and Omni Model Post-Training (PhD)

The Meta Reality Labs Research Team brings together researchers, developers, and engineers to create the future of AR, VR, and contextual AI. The Surreal Vision group at Reality Labs Research is seeking outstanding Research Scientist Interns to advance posttraining of vision-language and omni models. As an intern, you will develop reinforcement learning (RL) methods that improve multimodal reasoning, temporal understanding, and grounded decision-making. Applications include procedural and real-time task assistants for future contextual AI devices.

Responsibilities

• Plan and execute innovative research to advance the state of the art in multimodal posttraining and reinforcement learning.
• Collaborate with researchers to develop training pipelines, reward functions, experiments, and prototype systems.
• Benchmark RL methods against supervised fine-tuning and preference-optimization baselines, and analyze model behavior and generalization.
• Publish research findings at leading conferences and present research outcomes to internal and external audiences.

Minimum Qualifications

• Currently has, or is in the process of obtaining, a PhD in machine learning, computer vision, natural language processing, speech and audio processing, reinforcement learning, or a related field.
• Proven research contributions demonstrated by grants, fellowships, patents, or publications at leading workshops or conferences.
• Knowledge of deep learning and multimodal foundation models, with hands-on experience in VLM or omni model posttraining, particularly reinforcement learning.
• Experience implementing deep learning algorithms, training models, and designing benchmarks and evaluations.
• Programming experience in Python and deep learning frameworks such as PyTorch.
• Experience working and communicating across functions in a team environment.

Preferred Qualifications

• Experience with RL posttraining methods, reward modeling, and/or preference optimization.
• Experience with distributed training, rollout generation, and scalable evaluation of multimodal foundation models.
• Research experience in video understanding, temporal reasoning, and/or audio-visual learning.
• Demonstrated research experience through internships, work experience, open source contributions, and/or publications.
• Intent to return to a degree program after completion of the internship.