Remote AI Research Engineer – Multimodal RL & Agents

Tether Operations Limited; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Tether Operations Limited is seeking a highly skilled AI researcher to join our AI model team. You will design and advance reinforcement learning methods for multimodal data including text, images, and audio, aiming for robust, real-world intelligence and scalable training pipelines.

We value deep expertise in multimodal RL, model architectures, and PyTorch, with a track record of publications in top conferences.

Qualifications

• Master's degree required; PhD preferred in related field.
• Strong publication track record in top-tier conferences.
• Experience running large-scale RL experiments in multimodal/vision systems.

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.
• 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, along with a 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, with 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, with a focus on improving 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, with 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 solve core RL challenges in multimodal and vision tasks, such as sample inefficiency, exploration-exploitation tradeoffs, and training instability, along with experience designing robust evaluation frameworks and iterating on algorithmic improvements to advance agent performance.
• Proven track record of research publications in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV etc.

Skills

Reinforcement learning

Multimodal learning

PyTorch

Research experience

Deep learning

Education

Master's degree in Computer Science or related field

PhD in Machine Learning, NLP, Computer Vision, or related discipline (preferred)

Tools

PyTorch

Job description

Tether Operations Limited is seeking a highly skilled AI researcher to join our AI model team. You will design and advance reinforcement learning methods for multimodal data including text, images, and audio, aiming for robust, real-world intelligence and scalable training pipelines.

We value deep expertise in multimodal RL, model architectures, and PyTorch, with a track record of publications in top conferences.