AI Research Engineer (Multi-Modal Reinforcement Learning)

Overview

Accountabilities

• In this role, you will lead research and engineering efforts across multi-modal reinforcement learning systems while contributing to scalable AI infrastructure and experimentation frameworks.
• You will be responsible for advancing model performance and robustness through innovative algorithm design and rigorous evaluation practices.
• Conduct research on reinforcement learning methods for multi-modal systems, including diffusion-based and autoregressive model approaches.
• Design and build scalable RL infrastructure supporting distributed training and evaluation across complex multi-modal environments.
• Develop reward modeling strategies to improve alignment, training stability, and mitigate failure modes such as reward hacking.
• Create and curate simulation environments and datasets for training, benchmarking, and validating multi-modal RL models.
• Design and execute evaluation protocols to measure performance improvements and ensure reproducibility across experiments.
• Analyze model behavior across modalities, identifying bottlenecks in optimization, exploration, and cross-modal alignment.
• Explore and develop next-generation RL paradigms to enhance learning from environment feedback and improve SOTA performance.
• Publish research in leading AI conferences such as NeurIPS, ICML, ICLR, CVPR, and related venues.

Requirements

• The ideal candidate has a strong academic and practical background in machine learning, reinforcement learning, and multi-modal AI systems, with a proven record of research excellence and scalable system development.
• You are comfortable working at the frontier of AI research while building production-grade experimentation pipelines.
• Master’s degree in Computer Science or related field required; PhD preferred in ML, CV, NLP, or AI-related disciplines.
• Strong publication record in top-tier AI conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).
• Proven experience in large-scale reinforcement learning experiments, particularly in multi-modal or vision-centric systems.
• Deep understanding of reinforcement learning theory, optimization, and policy learning in high-dimensional environments.
• Strong hands-on experience with PyTorch and deep learning frameworks for multimodal AI systems.
• Experience building end-to-end RL pipelines including simulation, training, evaluation, and deployment.
• Ability to address core RL challenges such as sample efficiency, exploration-exploitation trade-offs, and training stability.
• Strong analytical and problem-solving skills with a research-driven, experimental mindset.

Benefits

• Competitive compensation package aligned with top-tier AI research talent
• Fully remote, global-first work environment
• Opportunity to work on frontier AI research problems at scale
• High-impact role influencing next-generation multimodal intelligence systems
• Collaboration with leading researchers and engineers in AI and reinforcement learning
• Access to large-scale experimentation infrastructure and research resources
• Strong culture of innovation, autonomy, and research publication support

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