Remote AI Research Engineer: Multimodal Agentic RL

Jobgether; Nederland; Remoto; EUR 100.000 - 165.000; Tempo pieno

Vantaggi offerti da questo lavoro

Fully Remote Work

International Collaboration

Cutting‑edge AI Research

Research Publications

Descrizione del lavoro

Jobgether is seeking an AI Research Engineer (Agentic Reinforcement Learning) based in the Netherlands to lead research and innovation in next-generation intelligent systems. The role focuses on developing reinforcement learning techniques for multimodal architectures and advancing diffusion-based and autoregressive models across text, image, and audio modalities.

You will design scalable training infrastructure, optimize reward mechanisms, and push state-of-the-art performance while addressing

Competenze

• Strong track record of AI research with publications in top conferences.
• Experience in large-scale reinforcement learning experiments across multimodal systems.
• Ability to translate research into practical AI solutions.

Mansioni

• Research and develop reinforcement learning algorithms for multimodal models including diffusion-based approaches.
• Design scalable reinforcement learning infrastructure for distributed training.
• Develop reward modeling strategies to improve training stability and align behavior.
• Create and maintain multimodal simulation environments and datasets for benchmarking.
• Publish and present findings at leading conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV.

Conoscenze

Reinforcement learning

Multimodal learning

Research

Formazione

Master's degree in Computer Science

PhD in ML/NLP/CS preferred

Strumenti

PyTorch

Descrizione del lavoro

Jobgether is seeking an AI Research Engineer (Agentic Reinforcement Learning) based in the Netherlands to lead research and innovation in next-generation intelligent systems. The role focuses on developing reinforcement learning techniques for multimodal architectures and advancing diffusion-based and autoregressive models across text, image, and audio modalities.

You will design scalable training infrastructure, optimize reward mechanisms, and push state-of-the-art performance while addressing