AI Research Engineer – Agentic Reinforcement Learning

🔥 1 hour ago

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

👻 Ghost score 25%

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📋 Description

• Conduct research on reinforcement learning algorithms for multimodal models, including diffusion-based approaches for image, autoregressive models for multimodal understanding, and unified multimodal frameworks • Design and build scalable, distributed reinforcement learning infrastructure for multimodal systems • Develop reward modeling strategies to improve training stability, align model behavior, and mitigate reward hacking • Create and curate multimodal simulation environments and datasets for training, evaluation, and benchmarking • Design and conduct benchmarking and evaluation protocols across multimodal tasks • Analyze and optimize policy performance by identifying bottlenecks in training, credit assignment, and cross-modal alignment • Investigate next-generation reinforcement learning paradigms that learn from environment feedback • Publish research findings in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV

🎯 Requirements

• 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 • 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, including online RL settings • Demonstrated impact on domain-specific decision-making and measurable improvements in policy performance • Deep understanding of reinforcement learning algorithms and optimization methods applied to vision and multimodal learning problems • Focus on policy stability, exploration, and sample efficiency in complex, high-dimensional environments involving images, video, and other modalities • Strong proficiency in PyTorch and deep learning frameworks for vision and multimodal AI • Hands-on experience building end-to-end RL pipelines covering simulation, training, evaluation, and deployment in production-grade systems • Demonstrated ability to apply empirical research to multimodal and vision RL challenges, including sample inefficiency, exploration-exploitation tradeoffs, and training instability • Experience designing robust evaluation frameworks and iterating on algorithmic improvements • Proven track record of research publications in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, etc. • Excellent English communication skills

🏖️ Benefits

• Remote work from anywhere in the world • Global, distributed team collaboration • Opportunity to collaborate with leading minds in fintech and AI • Professional/research opportunities through publication in top-tier conferences • Work on innovative digital finance, blockchain, AI, and peer-to-peer technology products

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