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, RecSys) 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. 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.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, RecSys) 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 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 researchersMeta 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.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 .Meta is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, fill out the Accommodations request form .