FAIR Research Lead for Reinforcement Learning for Design & Engineering Tasks

Meta's Fundamental AI Research lab (FAIR) is seeking an IC7 Research Engineer to lead a new exploratory research project to train LLMs to perform hard design and engineering tasks. As project lead, you will work with other researchers to build a research agenda around simulation and reinforcement learning from verifiable rewards (RLVR). This project may also involve the development of new pretraining and mid-training recipes. You'll work alongside experienced researchers and engineers to collect, develop, and deploy novel benchmarks and reinforcement learning environments. This is a technical leadership role requiring research engineering skills and the ability to work under your own initiative on a variety of open-ended machine learning challenges with high reliability. You will excel by maintaining high velocity while adapting to rapidly shifting priorities as we advance the technical research frontier. You'll need to be experienced in making decisions within a group environment and adaptive, tackling a wide variety of problems. If you are interested in exploring about defining the capabilities that drive AI progress and thrive in deadline-focused, high-impact research environments, we encourage you to apply for this exciting opportunity at the core of MSL.

Responsibilities
Demonstrate research and technical leadership in the context of exploratory research
* Build robust, reinforcement learning training pipelines that scale across multiple and complex end tasks
* Integrate, develop, and implement simulation environments for design and engineering tasks of interest

Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* 10+ years of experience in machine learning engineering, machine learning research, or a related technical role
* Proficiency in Python and experience with ML frameworks such as PyTorch
* Experience identifying, designing and completing medium to large technical features independently, without guidance
* Software engineering practices including version control, testing, and code review practices
* Demonstrated experience of working independently and adapting to rapidly changing priorities PhD degree in Computer Science, AI or relevant technical field
* A track record of high-quality research publications to top-tier ML conferences (ICLR, NeurIPS, etc)
* Previous technical leadership of exploratory research projects
* Domain expertise in hard design or engineering problems