Research Scientist - Agentic AI & 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

Medical, dental and vision benefits

Bonus

401K Plan

Generous paid time off, sick leave and

Holidays

Paid Parental Leave

Employee Assistance Program

Life insurance and disability

Job summary

Mohamed bin Zayed University of Artificial Intelligence seeks researchers and engineers to tackle challenges in training advanced agentic language models that reason and use tools to complete tasks on a computer. You will build high-quality training datasets, robust evaluations, and reinforcement learning infrastructure.

The role spans research and systems across algorithms, empirical work, and optimization.

Qualifications

• 2 years of experience in LLM training, fine-tuning, evaluations, or distributed ML.
• Python and PyTorch development experience.
• Experience using LLM agents for SWE tasks.
• Experience in designing and implementing algorithms from scratch.
• Experience in dataset curation and generation.
• Experience in building or contributing to training/serving infrastructure.
• Experience in designing and deep diving into evaluations.

Responsibilities

• Propose, prototype, and scale algorithms/systems to support agentic learning.
• Develop new paradigms for agentic behavior that go beyond current practices.
• Contribute to technical reports, research publications, and open‑source software.
• Collaborate with other teams to produce state‑of‑the‑art foundation models.

Skills

Python

PyTorch

LLM agents experience

Algorithm design

Distributed ML

Dataset curation

LLM inference

Reinforcement learning

Education

BS, MS, or PhD in Computer Science, Machine Learning

Tools

Ray

Triton

CUDA C++

Job description

Mohamed bin Zayed University of Artificial Intelligence seeks researchers and engineers to tackle challenges in training advanced agentic language models that reason and use tools to complete tasks on a computer. You will build high-quality training datasets, robust evaluations, and reinforcement learning infrastructure.

The role spans research and systems across algorithms, empirical work, and optimization.