Conduct research on reinforcement learning algorithms for multimodal models,
Design and build reinforcement learning infrastructure that supports scalable training,
Develop and refine reward modeling strategies,
Create and curate multimodal simulation environments and datasets,
Analyze and optimize policy performance across modalities,
Investigate and develop next-generation reinforcement learning paradigms,
Publish research findings in top-tier conferences.

A Master's degree in Computer Science or a related field is required;
A PhD in Machine Learning, NLP, Computer Vision, or a closely related discipline is preferred,
Proven experience running large-scale reinforcement learning experiments in multimodal and vision-centric systems,
Deep understanding of reinforcement learning algorithms,
Strong proficiency in PyTorch and deep learning frameworks for vision and multimodal AI,
Proven track record of research publications in top-tier conferences.

AI Research Engineer – Multi-Modal Reinforcement Learning

Key skills