AI Research Engineer

pThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Research Engineer (Agentic Reinforcement Learning) based in Belgium. /p pAs an AI Research Engineer specializing in Agentic Reinforcement Learning, you will drive research and innovation in next-generation intelligent systems.br/You will develop advanced reinforcement learning techniques that improve decision-making, adaptive behavior, and performance across multimodal AI architectures.br/Working with text, images, audio, and other data modalities, you will help build models capable of solving complex, real-world challenges.br/Your work will involve designing scalable training infrastructure, optimizing reward mechanisms, and improving the reliability of autonomous AI agents.br/You will explore cutting-edge approaches involving diffusion-based generative models, autoregressive architectures, and unified multimodal frameworks.br/Through rigorous experimentation and benchmarking, you will push the boundaries of model performance, efficiency, and generalization.br/This fully remote opportunity offers the chance to contribute to impactful AI research within a fast-moving, globally distributed technology environment. /p h3Accountabilities /h3 ul liResearch 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. /li liDesign and implement scalable reinforcement learning infrastructure supporting distributed training across complex multimodal systems. /li liDevelop and optimize reward modeling strategies to improve training stability, align model behavior with desired outcomes, and prevent reward hacking and related failure modes. /li liCreate, curate, and maintain multimodal simulation environments and training datasets to support robust model development, evaluation, and benchmarking. /li liDesign and implement rigorous evaluation protocols to measure model performance, monitor progress against established baselines, and validate research improvements. /li liAnalyze and optimize policy performance across different modalities by identifying bottlenecks in training, credit assignment, reward optimization, and cross-modal alignment. /li liInvestigate emerging reinforcement learning paradigms that enable AI agents to learn more effectively from environmental feedback. /li liImprove model exploration, decision-making, and adaptive behavior to achieve state-of-the-art performance in dynamic, real-world environments. /li liConduct large-scale experiments and translate empirical findings into practical algorithmic improvements. /li liPublish and present research findings at leading international conferences, including ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV. /li /ul h3Requirements /h3 ul liMaster'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. /li liStrong track record of AI research, including publications in leading international conferences. /li liDemonstrated experience conducting large-scale reinforcement learning experiments in multimodal and vision-centric systems, including online reinforcement learning environments. /li liProven ability to improve domain-specific decision-making and achieve measurable gains in policy performance. /li liDeep understanding of reinforcement learning algorithms and optimization techniques applied to vision and multimodal learning. /li liStrong knowledge of policy stability, exploration strategies, sample efficiency, and optimization in complex, high-dimensional environments involving images, video, and other modalities. /li liAdvanced proficiency in PyTorch and modern deep learning frameworks for computer vision and multimodal AI. /li liHands-on experience building end-to-end reinforcement learning pipelines covering simulation, model training, evaluation, and production deployment. /li liAbility to address fundamental reinforcement learning challenges, including sample inefficiency, exploration‑exploitation trade‑offs, and training instability. /li liExperience designing robust evaluation frameworks and using empirical research to continuously improve algorithmic performance. /li liEstablished research publication record at top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV. /li liStrong analytical thinking, scientific problem-solving skills, and the ability to translate advanced research into practical AI solutions. /li /ul h3Benefits /h3 ul liFully Remote Work: Work remotely from anywhere in the world, with a globally distributed team. /li liInternational Collaboration: Collaborate with talented researchers, engineers, and technology professionals across different countries and disciplines. /li liCutting-Edge AI Research: Work