Research Scientist - Reinforcement Learning

Institute of Foundation Models; Sunnyvale (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Institute of Foundation Models in Sunnyvale, CA, seeks a Research Scientist in our Reinforcement Learning team to shape scientific and technical directions for emergent capabilities in foundation models. You will prototype novel RL approaches, contribute to large-scale training infrastructure, and produce replicable code for public release.

You will build a productive research portfolio through internal and external collaborations, publish results, and present at conferences, while advancing our

Qualifications

• 3+ years of hands-on experience with reinforcement learning.
• Demonstrated ability to independently identify limitations of current practice and formulate/ enact solution strategies.
• Proactive mindset with ability to identify impactful research questions and execute with minimal supervision.
• Strong Python development skills with focus on research-grade code and scalable data pipelines.
• Experience applying novel RL algorithms to practical applications.
• Strong publication record in AI/RL venues or open-source contributions.

Responsibilities

• Develop novel research toward massive-scale self-play for foundation model training and agentic tasks.
• Initiate and pursue novel RL algorithmic approaches to drive emergent capabilities in Foundation Models.
• Full-stack engineering from data curation, model architecture to production-ready code.
• Contribute to technical reports and research publications.
• Represent the institute at conferences and open-source communities.
• Contribute to large-scale RL training and inference frameworks.
• Facilitate internal and external collaboration.

Skills

Reinforcement learning

Python

Research

Communication

Open-source contribution

Education

MSc/MEng or PhD in Machine Learning/CS

Tools

PyTorch

JAX

Job description

About The Institute Of Foundation Models

We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries.Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub forhigh-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AIpioneers.

Position Summary

As a Research Scientist within our Reinforcement Learning team, you will play a fundamental role in establishing our scientific and technical directions toward the development of emergent capabilities within Foundation Models. The role involves pioneering novel approaches within Reinforcement Learning to facilitate paradigm shifts in foundation modeling. The role involves prototyping and adapting novel approaches to learning from experience, contributing to large-scale RL training infrastructure, and produce replicable code for public release. You will also be expected to build and maintain a productive research portfolio, supported by internal and external collaborations.

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 anddeep 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).

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Salary Range

The posted salary range represents the company’s good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.