AI Research Engineer (Multi-Modal Reinforcement Learning)

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TetherFintech AI Research

100% Remote WorldwideFull-TimeMiddle

Salary not disclosed

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Job Details

Master's degree in Computer Science or related field; PhD in Machine Learning, NLP, or Computer Vision preferred.
Strong track record of AI research and publications in top-tier conferences.
Proven experience running large-scale reinforcement learning experiments in multimodal and vision-centric systems.
Deep understanding of RL algorithms and optimization methods applied to vision and multimodal learning.
Strong proficiency in PyTorch and deep learning frameworks for vision and multimodal AI.
Hands-on experience building end-to-end RL pipelines from simulation to deployment.
Demonstrated ability to apply empirical research to solve RL challenges like sample inefficiency and training instability.
Excellent English communication skills.

Conduct research on reinforcement learning algorithms for multimodal models, including diffusion-based approaches and unified frameworks.
Design and build reinforcement learning infrastructure that supports scalable, distributed training across multimodal systems.
Develop and refine reward modeling strategies that improve training stability and mitigate reward hacking.
Create and curate multimodal simulation environments and datasets for training and benchmarking.
Design and conduct rigorous benchmarking protocols to measure model performance and validate improvements.
Analyze and optimize policy performance across modalities by identifying bottlenecks in training and alignment.
Investigate next-generation reinforcement learning paradigms for superior performance in real-world environments.
Publish research findings in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV.

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