Role overview
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
What you'll work on
- 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
What we're looking for
- 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