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

Menlo Park, CA · Hybrid$219k – $301k
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About this role

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.


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, 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


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


$219,000/year to $301,000/year + benefits

Company at a glance

Meta is a company focused on shaping the future of human connection. Its mission is to build the future of human connection and the technology that makes it possible. Meta's technologies help people connect, find communities, and grow businesses. Facebook launched in 2004, changing the way people connect, and Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to drive the next evolution in social technology. The company also maintains a careers page at metacareers.com for job listings.

Team Size10000+
WorkspaceHybrid
IndustrySoftware Development
Location
Menlo Park, California, United States
Websitemetacareers.com
LinkedInLinkedIn

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