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 Italy. /p pAs 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. You will explore cutting-edge approaches involving diffusion-based generative models, autoregressive architectures, and unified multimodal frameworks. 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. /p h3Accountabilities /h3 ul lipResearch 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. /p /li lipDesign and implement scalable reinforcement learning infrastructure supporting distributed training across complex multimodal systems. /p /li lipDevelop and optimize reward modeling strategies to improve training stability, align model behavior with desired outcomes, and prevent reward hacking and related failure modes. /p /li lipCreate, curate, and maintain multimodal simulation environments and training datasets to support robust model development, evaluation, and benchmarking. /p /li lipDesign and implement rigorous evaluation protocols to measure model performance,



monitor progress against established baselines, and validate research improvements. /p /li lipAnalyze and optimize policy performance across different modalities by identifying bottlenecks in training, credit assignment, reward optimization, and cross-modal alignment. /p /li lipInvestigate emerging reinforcement learning paradigms that enable AI agents to learn more effectively from environmental feedback. /p /li lipImprove model exploration, decision-making, and adaptive behavior to achieve state-of-the-art performance in dynamic, real-world environments. /p /li lipConduct large-scale experiments and translate empirical findings into practical algorithmic improvements. /p /li lipPublish and present research findings at leading international conferences, including ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV. /p /li /ul h3Requirements /h3 ul lipMaster'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. /p /li lipStrong track record of AI research, including publications in leading international conferences. /p /li lipDemonstrated experience conducting large-scale reinforcement learning experiments in multimodal and vision-centric systems, including online reinforcement learning environments. /p /li lipProven ability to improve domain-specific decision-making and achieve measurable gains in policy performance. /p /li lipDeep understanding of reinforcement learning algorithms and optimization techniques applied to vision and multimodal learning. /p /li lipStrong knowledge of policy stability, exploration strategies, sample efficiency, and optimization in complex,



high-dimensional environments involving images, video, and other modalities. /p /li lipAdvanced proficiency in PyTorch and modern deep learning frameworks for computer vision and multimodal AI. /p /li lipHands-on experience building end-to-end reinforcement learning pipelines covering simulation, model training, evaluation, and production deployment. /p /li lipAbility to address fundamental reinforcement learning challenges, including sample inefficiency, exploration-exploitation trade-offs, and training instability. /p /li lipExperience designing robust evaluation frameworks and using empirical research to continuously improve algorithmic performance. /p /li lipEstablished research publication record at top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV. /p /li lipStrong analytical thinking, scientific problem-solving skills, and the ability to translate advanced research into practical AI solutions. /p /li /ul h3Benefits /h3 ul libFully Remote Work: /b Work remotely from anywhere in the world, with a globally distributed team. /li libInternational Collaboration: /b Collaborate with talented researchers, engineers, and technology professionals across different countries and disciplines. /li libCutting-Edge AI Research: /b Work on advanced reinforcement learning, multimodal architectures, generative AI, and next-generation intelligent systems. /li libResearch and Innovation: /b Explore new methodologies and contribute to state-of-the-art developments in artificial intelligence. /li libScientific Recognition: /b Opportunities to publish research and present findings at leading international AI and machine learning conferences. /li libProfessional Growth: /b Develop expertise in emerging AI technologies while contributing to challenging, high-impact research initiatives. /li libGlobal Technology Environment: /b Be part of an ambitious, fast-growing organization working across AI, digital finance, peer-to-peer technology, and other innovative fields. /li /ul #J-18808-Ljbffr

📌 AI Research Engineer (Agentic Reinforcement Learning) (Italia)
🏢 Jobgether
📍 Italia