Remote AI Research Engineer – Agentic RL for Multimodal

Jobgether; Netherlands; Remote; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Fully Remote

International Collaboration

Cutting‑Edge AI Research

Publication Opportunities

Professional Growth

Global Environment

Job summary

Jobgether is seeking an AI Research Engineer (Agentic Reinforcement Learning) based in the Netherlands through a partner company. You will drive research in agentic reinforcement learning for multimodal AI systems, including diffusion-based and autoregressive architectures.

This fully remote opportunity offers collaboration with a global team and opportunities to publish at top conferences. You will design scalable RL training infrastructure, optimize reward mechanisms, and push state-of-the-art

Qualifications

• Master's degree in Computer Science or a related field (PhD preferred).
• Proven AI research track record with publications at major conferences.
• Experience in large-scale reinforcement learning experiments in multimodal/vision-centric systems.

Responsibilities

• Develop RL algorithms for multimodal models including diffusion-based approaches.
• Design scalable RL infrastructure for distributed training.
• Create and curate multimodal training datasets and evaluation protocols.
• Publish and present research findings at major AI conferences.

Skills

PyTorch

Reinforcement Learning

Multimodal AI

Experiment design

Policy optimization

Research publications

Software pipelines

Education

Master's degree in CS or related

PhD in ML/ NLP / CV preferred

Tools

PyTorch

Deep learning frameworks

Job description

Jobgether is seeking an AI Research Engineer (Agentic Reinforcement Learning) based in the Netherlands through a partner company. You will drive research in agentic reinforcement learning for multimodal AI systems, including diffusion-based and autoregressive architectures.

This fully remote opportunity offers collaboration with a global team and opportunities to publish at top conferences. You will design scalable RL training infrastructure, optimize reward mechanisms, and push state-of-the-art