ML Engineer - Large-Scale Ads & Recommendations

AppLovin; Palo Alto (CA); On-site; USD 150,000 - 224,000; Full time

Benefits offered by this job

Health insurance

401(k) retirement plan

Unlimited discretionary time off

10 paid holidays per year

Paid sick leave

Equity eligible

Job summary

AppLovin seeks a Software Engineer with strong machine learning expertise to advance user signal and recommendation technologies across its advertising platform. You will work on large-scale ML problems spanning signals, representation learning, ranking, retrieval, model architecture, and optimization.

You will develop new ways to understand, represent, and utilize user signals and apply them to ranking and recommendation models.

Qualifications

• 4+ years of experience creating and deploying ML systems in production.
• Experience with recommendation, ranking, retrieval, or advertising applications.
• Experience building and training models on large-scale datasets.
• Strong fundamentals in model architectures, optimization, and evaluation.

Responsibilities

• Develop and improve user signals and representations for ML models in advertising and recommendations.
• Explore learning approaches for large-scale, sparse, noisy, heterogeneous signals.
• Improve signal quality, coverage, and impact on downstream models and performance.
• Develop representations that inform ranking, retrieval, prediction, and optimization systems.
• Advance large-scale recommendation systems from candidate retrieval to optimization.
• Explore new architectures to improve recommendation quality and ad performance.
• Scale learning and feature computation across data volumes and hardware constraints.
• Identify and solve ML problems in data quality, model quality, training stability, and serving performance.

Skills

4+ years experience

ML frameworks

Python

Production systems

Data processing

Model evaluation

Performance optimization

Education

Bachelor's degree in CS/CE/ML

Tools

PyTorch

TensorFlow

Job description

AppLovin seeks a Software Engineer with strong machine learning expertise to advance user signal and recommendation technologies across its advertising platform. You will work on large-scale ML problems spanning signals, representation learning, ranking, retrieval, model architecture, and optimization.

You will develop new ways to understand, represent, and utilize user signals and apply them to ranking and recommendation models.