AI Research Engineer (Multi-Modal Reinforcement Learning) - 100% Remote Worldwid

Responsibilities

• Conduct research on reinforcement learning algorithms for multimodal models, including diffusion-based approaches for image autoregressive models for multimodal understanding, and unified frameworks that integrate multiple modalities.
• Design and build reinforcement learning infrastructure that supports scalable, distributed training across multimodal systems while maintaining efficiency and reliability.
• Develop and refine reward modeling strategies that improve training stability, align model behavior with desired outcomes, and mitigate reward hacking and related failure modes.
• Create and curate multimodal simulation environments and datasets to support robust training, evaluation, and benchmarking of reinforcement learning systems.
• Design and conduct rigorous benchmarking and evaluation protocols to measure model performance, track progress against baselines, and validate improvements across multimodal tasks.
• Analyze and optimize policy performance across modalities by identifying bottlenecks in training, credit assignment, and cross-modal alignment.
• Investigate and develop next-generation reinforcement learning paradigms that more effectively learn from environment feedback, with the goal of achieving superior state-of-the-art (SOTA) performance.
• Publish research findings in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV etc.

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