This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
We are seeking a technically skilled GenAI scientist to join our team focused on Large Language Model (LLM) agents and model post-training, with a particular emphasis on reinforcement learning (RL). This role will be close to product applications and user impact, requiring full-stack knowledge.
Job Responsibility:
Design, implement, and optimize LLM-based agents for a variety of applications, leveraging the latest advances in generative AI
Apply reinforcement learning algorithms to improve LLM performance, safety, and alignment
Integrate models and orchestrations in production
Collaborate with cross-functional teams (research, engineering, product) to deploy and evaluate LLM agents in real-world scenarios
Analyze and interpret experimental results, iterate on model architectures, and drive continuous improvement
Contribute to the broader AI/ML community at Meta through knowledge sharing, code reviews, and technical mentorship
Lead and contribute to research and development of post-training methods, including RLHF (Reinforcement Learning from Human Feedback), reward modeling, and other feedback-based approaches
Requirements:
Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Generative AI, or a relevant technical field
Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
Good programming skills in Python and familiarity with large-scale distributed training
Familiarity to learn new programming languages quickly
Can design, implement, and evaluate RL algorithms in production or research settings
Problem-solving, communication, and collaboration skills
Nice to have:
Experience with RLHF, reward modeling, or other LLM post-training techniques
Experience working in cross-functional teams
Track record of publications or contributions to open-source projects in LLMs, RL, or related areas
Familiarity with safety, alignment, and evaluation challenges in generative AI