Foundational Research Scientist

AppLovin AppLovin · Media · Palo Alto, CA · Platform Engineering

This role focuses on foundational research for new recommendation models and paradigms, leveraging rich live user data and large-scale compute. It's an academic-industrial hybrid research group aiming to shape the next generation of recommendation science and deploy work into real products.

What you'd actually do

  1. Drive foundational research to create new recommendation models and paradigms.
  2. Leverage rich live user data and large-scale compute to validate models rapidly.
  3. Collaborate closely with engineering and product teams to operationalize research.
  4. Publish findings and contribute to the broader ML and RecSys community.

Skills

Required

  • PhD (or equivalent research experience) in CS, ML, Statistics, or related field.
  • Strong background in deep learning.
  • Proven track record of research excellence (publications, awards, impactful projects).
  • Proficiency in Python and modern ML frameworks (PyTorch).
  • Experience with large-scale data and experimentation.

Nice to have

  • Publications in top venues (NeurIPS, ICML, ICLR, KDD, RecSys, SIGIR, WWW).
  • Experience with sequential modeling, representation learning, or causal inference.
  • Knowledge of online experimentation and evaluation methodologies.
  • Industry experience deploying ML in production systems.

What the JD emphasized

  • modern recommendation stack was established about a decade ago
  • academic-industrial hybrid research group
  • rich live user data
  • large-scale compute
  • rich real-time data
  • Massive compute & infrastructure
  • Rapid experimentation
  • Direct impact
  • Balanced path
  • academic rigor with industrial scale

Other signals

  • Foundational research
  • new recommendation models and paradigms
  • rich live user data
  • large-scale compute
  • academic-industrial hybrid research group