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MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) · 5 months ago

Research Scientist - Reinforcement Learning

Sunnyvale, CA

Full-time

Onsite

Mid, Senior Level

$300K/yr - $600K/yr

3+ years exp

The Institute of Foundation Models is a dedicated research lab focused on advancing research and building capabilities in foundation models. As a Research Scientist in the Reinforcement Learning team, you will develop novel approaches to reinforcement learning, contribute to large-scale training infrastructure, and maintain a productive research portfolio while collaborating with internal and external partners.

Artificial Intelligence (AI)Higher EducationUniversities

H1B Sponsored

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

Qualification

Reinforcement LearningPythonDeep Learning FrameworksLarge-scale Model TrainingResearch PublicationCollaboration SkillsProblem-solving SkillsCommunication Skills

Required

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

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

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)