Foundational Research Scientist

App Lovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences.

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At App Lovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

Fortune recognizes App Lovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years ).

At App Lovin, we’re powering the future of product discovery and engagement through cutting‑edge machine learning.
Recommender systems quietly shape the daily lives of billions of people—deciding what we watch, read, play, and buy. They don’t just influence culture; they drive
trillions of dollars in market value across ads, commerce, and streaming, fueling
economic growth and job creation worldwide
, and the space is still growing at
double-digit rates annually
.

The modern recommendation stack was established about a decade ago, but we believe the next era of models will look fundamentally different. We’re assembling a research team dedicated to shaping that future.

We’re creating a world‑class academic‑industrial hybrid research group to advance recommender systems. Unlike academia, your work won’t live only in papers — it will be deployed into real products, used by millions, and validated s is your chance to push the science forward and see your ideas transform how the world discovers content.

• Drive foundational research to create new recommendation models and paradigms.
• Collaborate closely with engineering and product teams to operationalize research.
• Publish findings and contribute to the broader ML and Rec Sys community.

• Rich real‑time data
  : access to large‑scale, diverse, and dynamic user interactions.
• Rapid experimentation
  : immediate feedback through A/B testing and online evaluation.
• Direct impact
  : see your models shape user experiences and business outcomes.
• Cross‑disciplinary collaboration
  : partner with product, design, and engineering teams.
• Balanced path
  : combine scientific exploration with practical deployment.

We’re looking for rising researchers with strong academic backgrounds and a desire to have real‑world impact.

Minimum Qualifications

• 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, Rec Sys, 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.

• The opportunity to shape the next generation of recommendation science
  .
• A research culture that combines academic rigor with industrial scale
  .
• Access to world‑class infrastructure and live experimentation loops.
• A collaborative environment that rewards both innovation and execution.

App Lovin provides a competitive total compensation package with a pay‑for‑performance rewards approach. Total compensation at App Lovin is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Depending on the position offered, equity, and other forms of incentive compensation (as applicable) may be provided as part of a total compensation package, in addition to dental, vision, and other benefits.

CA Base Pay Range

$252,000 - $400,000 USD

App Lovin has become aware of a scam targeting jobseekers with fake “app optimization” and similar roles. We do not ask our candidates to download apps or…