AI Research Engineer (Agentic Reinforcement Learning)

Accountabilities

In this role, you will conduct advanced research and develop scalable reinforcement learning systems for multimodal AI applications. Your main responsibilities will include:

• Research and develop reinforcement learning algorithms for multimodal models, including diffusion-based approaches for image generation, autoregressive models for multimodal understanding, and unified architectures integrating multiple modalities.
• Design and implement scalable reinforcement learning infrastructure supporting distributed training across complex multimodal systems.
• Develop and optimize reward modeling strategies to improve training stability, align model behavior with desired outcomes, and prevent reward hacking and related failure modes.
• Create, curate, and maintain multimodal simulation environments and training datasets to support robust model development, evaluation, and benchmarking.
• Design and implement rigorous evaluation protocols to measure model performance, monitor progress against established baselines, and validate research improvements.
• Analyze and optimize policy performance across different modalities by identifying bottlenecks in training, credit assignment, reward optimization, and cross-modal alignment.
• Investigate emerging reinforcement learning paradigms that enable AI agents to learn more effectively from environmental feedback.
• Improve model exploration, decision-making, and adaptive behavior to achieve state-of-the-art performance in dynamic, real-world environments.
• Conduct large-scale experiments and translate empirical findings into practical algorithmic improvements.
• Publish and present research findings at leading international conferences, including ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV.

Requirements

The ideal candidate combines strong theoretical knowledge of reinforcement learning with hands-on experience developing and optimizing large-scale multimodal AI systems. The following qualifications and technical skills are expected:

• Master's degree in Computer Science or a related discipline. A PhD in Machine Learning, Natural Language Processing, Computer Vision, or a closely related field is preferred.
• Strong track record of AI research, including publications in leading international conferences.
• Demonstrated experience conducting large-scale reinforcement learning experiments in multimodal and vision-centric systems, including online reinforcement learning environments.
• Proven ability to improve domain-specific decision-making and achieve measurable gains in policy performance.
• Deep understanding of reinforcement learning algorithms and optimization techniques applied to vision and multimodal learning.
• Strong knowledge of policy stability, exploration strategies, sample efficiency, and optimization in complex, high-dimensional environments involving images, video, and other modalities.
• Advanced proficiency in PyTorch and modern deep learning frameworks for computer vision and multimodal AI.
• Hands-on experience building end-to-end reinforcement learning pipelines covering simulation, model training, evaluation, and production deployment.
• Ability to address fundamental reinforcement learning challenges, including sample inefficiency, exploration-exploitation trade-offs, and training instability.
• Experience designing robust evaluation frameworks and using empirical research to continuously improve algorithmic performance.
• Established research publication record at top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV.
• Strong analytical thinking, scientific problem-solving skills, and the ability to translate advanced research into practical AI solutions.

Benefits

• Fully Remote Work: Work remotely from anywhere in the world, with a globally distributed team.
• International Collaboration: Collaborate with talented researchers, engineers, and technology professionals across different countries and disciplines.
• Cutting-Edge AI Research: Work on advanced reinforcement learning, multimodal architectures, generative AI, and next-generation intelligent systems.
• Research and Innovation: Explore new methodologies and contribute to state-of-the-art developments in artificial intelligence.
• Scientific Recognition: Opportunities to publish research and present findings at leading international AI and machine learning conferences.
• Professional Growth: Develop expertise in emerging AI technologies while contributing to challenging, high-impact research initiatives.
• Global Technology Environment: Be part of an ambitious, fast-growing organization working across AI, digital finance, peer-to-peer technology, and other innovative fields.