Opportunities · Research opportunity
Research Scientist Intern - VLM and Omni Model Post-Training - PhD
Meta
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- Pay
- $50/hr
- When
- Summer 2027
- Where
- Sunnyvale, CA
What you'll do
- As an intern, you will develop reinforcement learning (RL) methods that improve multimodal reasoning, temporal understanding, and grounded decision-making.
- Developing RL posttraining algorithms for VLMs and omni models, including on-policy optimization and learning from expert feedback.
- Designing reward models and verifiable rewards for multimodal reasoning, temporal grounding, and calibrated decisions.
- Building evaluations and ablations that measure grounded model improvements and identify reward hacking and failures under ambiguous evidence.
About Meta
The Meta Reality Labs Research Team brings together researchers, developers, and engineers to create the future of AR, VR, and contextual AI.
Full posting
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.
Your research project may cover or relate to the following topics:
- Developing RL posttraining algorithms for VLMs and omni models, including on-policy optimization and learning from expert feedback.
- Designing reward models and verifiable rewards for multimodal reasoning, temporal grounding, and calibrated decisions.
- Combining supervised fine-tuning, preference learning, and RL to improve data efficiency, training stability, and generalization.
- Posttraining with streaming video, speech, and environmental audio to improve reasoning.
- Building evaluations and ablations that measure grounded model improvements and identify reward hacking and failures under ambiguous evidence.
Our internships are twelve (12) to twenty-four (24) weeks long and we have flexible start dates.
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Posted
Oct 13, 2026
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