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

Meta; Sunnyvale (CA); On-site; USD 40,000 - 60,000; Part time

Job summary

Meta Reality Labs Research invites outstanding Research Scientist Interns to advance posttraining of vision-language and omni models. You will develop RL methods to improve multimodal reasoning, temporal understanding, and grounded decision-making for contextual AI devices.

Internships run twelve to twenty-four weeks with flexible start dates. Applicants should have a PhD in machine learning, computer vision, natural language processing, speech and audio processing, or reinforcement learning,

Qualifications

• PhD or in progress in ML, CV, NLP, or related field.
• Demonstrated research contributions via grants, fellowships, patents, or publications.
• Hands-on experience with deep learning and multimodal models, RL posttraining.
• Experience implementing DL algorithms, training models, and designing benchmarks.

Responsibilities

• Develop RL posttraining methods for vision-language and omni models.
• Design reward models and evaluation benchmarks for multimodal reasoning.
• Collaborate across teams to advance contextual AI devices.

Skills

Python

PyTorch

Reinforcement Learning

Multimodal Models

Team Collaboration

Education

PhD in ML / CV / NLP / speech & audio / RL

Tools

PyTorch

Job description

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.

Requirements and 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 such as NeurIPS, ICML, CVPR, ICLR, ICCV, ECCV, ACL, EMNLP, NAACL, ICASSP, and Interspeech
• 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
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
• 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