Research Scientist - Reinforcement Learning

Salary Range: 150000 to 450000 (Currency: USD) (Pay period: per-year-salary)

Key Responsibilities

• Develop novel research toward massive scale self-play for foundation model training, agentic tasks, and imbuing models with the capability to proactively learn from its environment.

• Initiate and pursue novel reinforcement learning algorithmic approaches to define and drive emergent capabilities in Foundation Models.

• Full-stack engineering from data curation, model architecture and algorithm design, to final production of models for end-users using high quality (documented, tested, maintainable) code.

• Contribute to technical reports and research publications.

• Represent MBZUAI at industry conferences and events, showcasing the institution’s technology and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.

• Proactively engage with the open-source community.

• Contribute to large-scale reinforcement learning training and inference frameworks.

• Facilitate internal and external collaboration

Academic Qualifications

MSc/MEng or PhD Degree (or equivalent experience) in Machine Learning, Computer Science or related fields.

Professional Experience

Minimum

• 3+ years of hands-on experience with reinforcement learning

• Demonstrated ability to independently identify limitations of current practice (internal and external), formulate and enact solution strategies for improvement.

• Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.

• Strong Python development skills with a focus on research-grade code and scalable data pipelines.

• Practical experience implementing complex mathematical concepts into reliable, well-documented code.

• Experience applying novel RL algorithms to practical applications.

• Strong experience contributing to academic and/or open-source research through publication, GitHub contributions, or professional presentations.

• Strong communication and collaboration skills for effective cross-functional work.

Preferred Qualifications

• Strong systems and engineering expertise in deep learning frameworks such as PyTorch, Jax, etc.

• Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.

• Familiarity with current RL+LLM training libraries

• Experience training policies in self-play, possibly demonstrated by publication, blog post, public code.

• Experience working with Diffusion Models in RL, possibly demonstrated by publication, blog post, public code.

• Strong publication record in leading AI and RL venues (e.g.ICLR, ICML, NeurIPS, RLC, JMLR, TMLR)

• Familiarity with performance constraints in production environments and the trade-offs in model design and execution.

• Prior contributions to open-source ML research or data tools.

• Demonstrated ability to solve complex system-level challenges and debug failures across training/inference stack (e.g. memory issues, deadlocks, I/O bottlenecks, multi-node communication failures).