Meta is seeking a Research Scientist to join Meta Superintelligence Labs (MSL). The position is with Fundamental AI Research (FAIR), AI research lab developing new architectures, capabilities and training paradigms to advance the frontier of AI. Researchers will drive impact by building the next generation of frontier agentic systems and products that connect billions of users, while contributing high quality code and reproducible results to the open source community. There is also an opportunity to publish state-of-the-art research. They will work with an interdisciplinary team of scientists, engineers, and cross-functional partners, and will have access to cutting-edge technology, resources, and research facilities.
Responsibilities
- Perform research to advance the science and the technology of intelligent machines
- Work towards long-term ambitious research goals, while identifying intermediate milestones
- Influence the progress of relevant research communities by producing publications
- Open-source high-quality code and generate reproducible science
Minimum Qualifications
- Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- First-authored publications at peer-reviewed conferences, such as ICML, NeurIPS, ICLR, ACL, EMNLP, and other venues, reflecting experience in machine learning
- Research background in machine learning, artificial intelligence, theoretical physics, applied mathematics, or related areas
- Proficiency in Python and deep learning frameworks such as PyTorch or comparable tools, with experience utilizing AI-powered coding assistants in development workflows
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Currently has, or is in the process of obtaining a PhD in Computer Science, Applied Mathematics, Theoretical Physics, or a relevant technical field
- Experience with large-scale model training, including distributed training strategies, mixed-precision training, or efficient fine-tuning methods such as parameter-efficient adaptation techniques
- Experience collaborating with product or engineering teams to transition research prototypes into deployed AI systems at scale
- Track record of contributing to peer-reviewed AI research publications or open-source machine learning projects
- Familiarity with generative AI systems, including language model pre-training, instruction tuning, reinforcement learning from human feedback, or multimodal architectures