Applied Research Scientist

AppLovin

Quick summary

Work type: On-site
Location: Palo Alto, California
Salary: $166,000–$300,000 / yr
Posted: 62 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $197k

This role $233k

$143k most similar roles pay here $317k

This role pays more than 72% of similar roles. Most pay $159,437–$234,150 — the shaded band above. At the midpoint, this role pays about $233k versus about $197k for comparable roles.

Based on 240 similar postings.

Employer

About AppLovin

AppLovin enables businesses to advertise profitably with marketing technologies that attract customers, increase revenue, and track ad performance.

AppLovin currently has 16 open roles on FindRole.

Listed pay typically runs $125,000–$206,500 across 12 roles with salary data.

Most-posted roles

View all roles at AppLovin

At a glance

TL;DR · Applied Research Scientist

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Join AppLovin as an Applied Research Scientist, a senior-level role within our dynamic research and engineering team, where you will build advanced ads delivery systems that integrate various data types to deliver highly relevant products to users. Your daily tasks include developing state-of-the-art machine learning models, processing massive datasets, and conducting large-scale A/B tests to optimize ad performance. Proficiency in Python with experience using PyTorch is essential, along with strong mathematical skills and a background in handling big data and distributed computing frameworks. Ideal candidates have prior experience with recommendation systems and additional programming languages like C++ or Java. This role offers an unparalleled opportunity to drive significant business growth within a fast-paced environment that values rapid execution and immediate impact.

Skills

Python PyTorch Machine Learning Deep Learning C++ Java Docker CI/CD A/B Testing Kafka Hadoop Spark PostgreSQL MongoDB

What you'll do

Develop and implement advanced machine learning models to enhance ad delivery systems.
Process and analyze large datasets containing various types of data (tabular, text, image, video).
Optimize system performance to handle high traffic volumes and rapid data processing needs.
Conduct A/B testing to evaluate the effectiveness of new features or improvements.
Integrate diverse data sources into a cohesive recommendation model for ads delivery.

What we're looking for

Experience with state-of-the-art Machine Learning/Deep Learning models.
Strong programming skills in Python and proficiency with PyTorch.
Ability to handle large datasets and distributed computing frameworks.
Strong mathematical background for data interpretation and analysis.
Prior experience working on recommendation systems or similar projects.

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