AI Research Engineer (Agentic Reinforcement Learning)

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

This 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 Netherlands.

As 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.

Responsibilities

• 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.

Requirements

• Master'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.
• 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.

Benefits

• Fully Remote Work: Work remotely from anywhere in the world, with a globally distributed team.
• 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.