Reinforcement Learning Research Scientist for Foundation Models

Mohamed bin Zayed University of Artificial Intelligence; Sunnyvale (CA); On-site; USD 150,000 - 450,000; Full time

Benefits offered by this job

Comprehensive medical, dental, and 401

Generous paid time off

Parental Leave

Job summary

Mohamed bin Zayed University of Artificial Intelligence is seeking a Research Scientist to advance reinforcement learning for foundation models. You will prototype learning-from-experience methods, contribute to large-scale training systems, and publish open code.

You will build a productive research portfolio through internal and external collaborations, and present MBZUAI’s capabilities at industry venues while engaging with the open-source community.

Qualifications

• PhD or equivalent research experience in ML/CS or related fields.
• 3+ years hands-on experience with reinforcement learning.
• Strong Python development skills with research-grade code.
• Experience publishing in AI/RL venues or contributing to open source.
• Ability to identify research questions and execute with minimal supervision.

Responsibilities

• Develop novel RL research toward emergent capabilities for foundation models.
• Prototype and adapt learning-from-experience approaches for scaling.
• Contribute to large-scale RL training infrastructure and public releases.
• Build and maintain a productive research portfolio with collaborations.
• Represent MBZUAI at conferences and engage with the open-source community.

Skills

Reinforcement Learning

Python

Open Source Collaboration

Research Portfolio Development

Technical Communication

Education

MSc/MEng or PhD in ML/CS or related field

Tools

PyTorch

Jax

LLM training libraries

Diffusion Models

Job description

Mohamed bin Zayed University of Artificial Intelligence is seeking a Research Scientist to advance reinforcement learning for foundation models. You will prototype learning-from-experience methods, contribute to large-scale training systems, and publish open code.

You will build a productive research portfolio through internal and external collaborations, and present MBZUAI’s capabilities at industry venues while engaging with the open-source community.