Visiting Scholar — Post Training & Research (Discovery AI Lab)

Discovery AI Lab is pioneering large language model (LLM) technologies across the full ML lifecycle — pre-training, mid-training, and post-training — to replace traditional recommendation pipelines with generative AI. Our work spans preference alignment and reinforcement learning for recommendation systems, self-improving agentic AI, and efficient training and inference at scale. We publish at top venues (NeurIPS, ICML, ICLR, Rec Sys) while shipping research directly into products that serve billions of users.
We are seeking a Visiting Scholar to join the team for a 12-month, full-time research engagement. This role offers the opportunity to lead cutting-edge research embedded within a world-class team, with access to Meta-scale infrastructure, data, and compute.
Visiting Scholar — Post Training & Research (Discovery AI Lab) Responsibilities
Lead research on post-training algorithms for generative recommendation systems, including preference alignment methods (e.g., DPO, GRPO, SimPO) adapted for multi-objective recommendation signals.
Design and develop self-improving agent frameworks that leverage multi-agent collaboration, LLM self-correction, and continuous-learning loops.
Advance efficient inference techniques — including quantization, compression, and distillation — for large-scale generative and Mixture-of-Experts recommendation models.
Collaborate with research scientists and engineers to translate research into production-ready systems at Meta scale.
Mentor research scientists and engineers on the team, upleveling internal capabilities in post-training and agentic AI.

Minimum Qualifications:

PhD in Computer Science, Machine Learning, Natural Language Processing, or a related field
Active faculty appointment or equivalent research position at a university or research institution
Demonstrated publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, Rec Sys)
Expertise in one or more of: post-training methods (RLHF, preference optimization, reward modeling), large language models, or agentic AI systems
Experience conducting research in collaborative, team-based environments
Available for a full-time, 12-month on-site or hybrid engagement
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Preferred Qualifications:

Tenured or tenure-track faculty position
Research focus at the intersection of LLMs and recommendation systems
Experience with reinforcement learning for language models or multi-agent systems
Published work on model compression, quantization, or efficient inference for large-scale models
Prior industry research experience (internship or collaboration) with production ML systems
Track record of mentoring graduate students or junior researchers

About Meta

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 Whats App 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.
For those who live in or expect to work from California if hired for this position, please click here for additional information.
United States of America: $219,000/year to $301,000/year + benefits
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.

Equal Employment Opportunity:

Meta is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. You may view our Equal Employment Opportunity notice here.
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