This role pays less than
65%
of similar roles. Most pay
$158,862–$241,750
— the shaded band above.
At the midpoint, this role pays about
$187k
versus about
$200k
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
9 open roles
on FindRole.
Listed pay typically runs
$119,000–$179,000
across 9 roles with salary data.
As a Software Engineer, Machine Learning, you will join the team to advance user signal and recommendation technologies across the advertising platform. You will work on large-scale machine learning problems including representation learning, ranking, retrieval, model architecture, and optimization. Your daily responsibilities involve developing new ways to represent and utilize user signals, building scalable tools for feature evaluation, and improving training and inference efficiency. You will manage the full machine learning stack from feature development to production deployment while solving challenges in data integrity and serving performance. The role requires proficiency with PyTorch or TensorFlow and experience with deep learning architectures for recommendation systems. You will address technical problems involving sparse, noisy, and heterogeneous user signals to improve the relevance of advertising systems at scale through advanced modeling, multi-task learning, and distributed training on GPUs.
What does a Software Engineer earn in California?
Median $214000 from 761 postings across 67 companies.
Develop and improve user signals, features, and representations for large-scale machine learning models.
Implement advanced recommendation systems across candidate retrieval, ranking, prediction, and optimization stages.
Explore new model architectures and learning approaches to enhance advertising performance and quality.
Build scalable tools and frameworks for feature evaluation, model training, experimentation, and deployment.
Optimize training and inference efficiency by identifying bottlenecks in computation, data loading, and hardware utilization.
Design and analyze offline and online experiments to measure the impact of new features on business outcomes.
Solve complex problems regarding data integrity, training stability, and serving performance for large-scale systems.
What we're looking for
Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
4+ years of experience developing and deploying machine learning systems in production environments.
Experience with machine learning or deep learning in areas such as recommendation, ranking, retrieval, prediction, advertising, representation learning, or related applications.
Experience developing and training machine learning models using large-scale datasets.
Strong understanding of machine learning fundamentals including model architectures, optimization, representation learning, feature engineering, and model evaluation.
Strong programming and software engineering skills with experience building reliable production systems.
Experience with modern deep learning frameworks such as PyTorch or TensorFlow.
Experience diagnosing and solving problems involving data/feature quality, model quality, training, or serving performance.
LLM
PyTorch
TensorFlow
JAX
Transformers
MoE
Computer Vision
Natural Language Processing
Distributed Data Processing
Machine Learning
Deep Learning
OCR
Synthetic Data Generation
Data Curation
High Performance Computing
PyTorch
TensorFlow
Machine Learning
Deep Learning
LLM
Feature Stores
Data Pipelines
CI/CD
Information Retrieval
Recommender Systems
Multi-modal Data
Agentic AI