AI Research Engineer – Agentic Reinforcement Learning
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Job Description
AI Research Engineer advancing multimodal reinforcement learning, reward modeling, and scalable training infrastructure. Researching intelligent systems for Tether’s global blockchain-based digital finance ecosystem.
Responsibilities:
- 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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