About the position
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
for high-performance computing in deep learning, driving impactful
discoveries that inspire the next generation of AI pioneers.
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 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).
$150,000 - $450,000 a year
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.
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.
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
for high-performance computing in deep learning, driving impactful
discoveries that inspire the next generation of AI pioneers.
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 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).
$150,000 - $450,000 a year
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.
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.
This listing was collected from a public source and is reproduced here for
information only. Always confirm the details on the original posting before applying.
View the original posting