AI Research Engineer (Agentic Reinforcement Learning)
Job description
An AI Research Engineer role focused on agentic reinforcement learning for multimodal AI systems, driving research and innovation in next-generation intelligent systems. The position involves developing advanced RL techniques and scalable training infrastructure for autonomous AI agents.
At a glance
- Work mode
- Remote · France
- Languages
- English
What you'll do
- 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
What we're looking for
- Master's degree in Computer Science or a related discipline
- 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
Nice to have
- PhD in Machine Learning, Natural Language Processing, Computer Vision, or a closely related field
Benefits
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
- Fully Remote Work: Work remotely from anywhere in the world, with a globally distributed team
How to get hired
- 1AI-powered matching process reviews applications against the role's core requirements
- 2Shortlist shared directly with the hiring company
- 3Final decision and next steps (interviews, assessments) managed by the hiring company's internal team