Postdoctoral Researcher, Fundamental AI Research (phd)

Meta Meta · Big Tech · Menlo Park, CA

Postdoctoral Researcher in Fundamental AI Research (FAIR) at Meta, focusing on cutting-edge research in AI/ML, deep learning, NLP, computer vision, and generative modeling, with a goal of understanding intelligence and achieving advanced machine intelligence. The role involves experimental design, code development, evaluations, publications, and contributing to Meta's product development.

What you'd actually do

  1. Directly contribute to experiments, including designing experimental details, developing reusable code, running evaluations, and organizing results
  2. Contribute to publications and open-sourcing efforts
  3. Work with a large team and play a significant role in healthy cross-functional collaboration
  4. Help identify long-term ambitious research goals as well as intermediate milestones
  5. Prioritize research that can be applied to Meta's product development

Skills

Required

  • PhD degree in Computer Science or a similar field
  • Research background in machine learning, natural language processing, computer vision, computational statistics, applied mathematics, physics, or related areas
  • Track record of recent and consistent publications reflecting expertise in theoretical and/or empirical research
  • Experience solving analytical problems using analytic and quantitative approaches
  • First-authored publications at peer-reviewed conferences (ICML, ICLR, NeurIPS, CVPR, ECCV, ICCV, ACL, NAACL, EMNLP, or similar)
  • Experience communicating research for public audiences of peers
  • Experience working and communicating cross functionally in a team environment
  • Experience solving complex problems and comparing alternative solutions, trade-offs, and different perspectives to determine a path forward
  • Research and engineering experience demonstrated via grants, fellowships, patents, internships, work experience, and/or coding competitions

Nice to have

  • deep learning
  • computer perception
  • natural language processing
  • representation learning
  • optimization
  • reinforcement learning
  • privacy in machine learning
  • security of AI systems
  • foundations of generative modeling
  • compression and information theory
  • social implications of AI

What the JD emphasized

  • First-authored publications at peer-reviewed conferences (ICML, ICLR, NeurIPS, CVPR, ECCV, ICCV, ACL, NAACL, EMNLP, or similar)

Other signals

  • cutting-edge research
  • producing new science
  • advanced machine intelligence