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

About the job

As a member of the AI model team, you will drive innovation in multi-modal reinforcement learning to advance next-generation intelligent systems. Your work will focus on optimizing decision-making and adaptive behavior across integrated data modalities such as text, images and audio to deliver enhanced intelligence, robust performance, and domain-specific capabilities for real-world challenges.

Responsibilities

• Develop and scale reinforcement learning techniques within complex multi-modal architectures, including diffusion-based generative models and autoregressive models for multimodal understanding, as well as resource-efficient models designed for constrained hardware environments.
• Conduct research on reinforcement learning algorithms for multimodal models, spanning diffusion models for image autoregressive models for multimodal reasoning, and unified multimodal frameworks.
• Design and develop RL infrastructure and reward modeling strategies to enable efficient large-scale training, improve training stability, and mitigate reward hacking and related failure modes.
• Curate multi-modal simulation environments and training datasets, improve baseline policy performance across modalities, and identify and resolve bottlenecks in multi-modal learning and reward optimization.
• Explore next-generation reinforcement learning paradigms that more directly and effectively learn from environment feedback.

Qualifications

• Deep expertise in designing multi-modal reinforcement learning systems.
• Strong background in advanced model architectures.
• Hands-on, research-driven approach to building and deploying novel algorithms and training frameworks.