Our partner is looking for an AI Research Engineer (Agentic Reinforcement Learning) based in Spain. As an AI Research Engineer specializing in Agentic Reinforcement Learning, you will drive research and innovation in next-generation intelligent systems. You will develop advanced reinforcement learning techniques that improve decision-making, adaptive behavior, and performance across multimodal AI architectures. Working with text, images, audio, and other data modalities, you will help build models capable of solving complex, real-world challenges. Your work will involve designing scalable training infrastructure, optimizing reward mechanisms, and improving the reliability of autonomous AI agents. Through rigorous experimentation and benchmarking, you will push the boundaries of model performance, efficiency, and generalization. This fully remote opportunity offers the chance to contribute to impactful AI research within a fast-moving, globally distributed technology environment. 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. art performance in dynamic, real?Publish and present research findings at leading international conferences, including ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV. 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. centric systems, including online reinforcement learning environments. making and achieve measurable gains in policy performance. 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. offs, and training instability. Experience designing robust evaluation frameworks and using empirical research to continuously improve algorithmic performance. Established research publication record at top?solving skills, and the ability to translate advanced research into practical AI solutions. Fully Remote Work: Work remotely from anywhere in the world, with a globally distributed team. Edge AI Research: Research and Innovation: Opportunities to publish research and present findings at leading international AI and machine learning conferences. impact research initiatives. General Technology Environment: growing organization working across AI, digital finance, peer?to?peer technology, and other innovative fields. #

📌 Research engineer (España)
🏢 Jobgether
📍 España