Ifm Us
Research Scientist - Reinforcement Learning
Sunnyvale, CA
Sponsorship not specified$150k-$450kDetected 356 days ago
PythonNode.jsFull-Stack DevelopmentAlgorithmsGitMachine LearningDeep LearningPyTorchLLMsResearchCommunicationCollaborationProblem Solving
About the role
- 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.
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.
- 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.
Requirements
- 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.
- Experience applying novel RL algorithms to practical applications.
Nice to have
- 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)
- 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).
Compensation
- $150k-$450k
Benefits
- Initiate and pursue novel reinforcement learning algorithmic approaches to define and drive emergent capabilities in Foundation Models.
- 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.
- Contribute to large-scale reinforcement learning training and inference frameworks.
- 3+ years of hands-on experience with reinforcement learning
This listing is sourced directly from Ifm Us's careers page and normalized into a canonical job model.