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Language Research Scientist

Switzerland, Zurich · Job Posted January 23, 2026
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Job Description

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
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