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.

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.

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, 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 researchers