AppLovin
ML Infrastructure Engineer
About this role
AppLovin seeks an ML Infrastructure Engineer to build and maintain scalable distributed systems that support machine learning model training, serving, and optimization. You'll collaborate with research and backend teams to enhance performance and reliability of the bidding ecosystem infrastructure.
What you'll do
- Design and develop large-scale distributed systems for ML infrastructure
- Maintain and optimize the model delivery pipeline including training and serving
- Collaborate with research science and backend teams on product roadmap
- Improve performance and latency of online models
- Provide scalable infrastructure with high throughput and low latency
- Mentor and influence team members on technical challenges
What they're looking for
- Distributed systems design
- C++, Python, or Golang
- Computer science fundamentals (data structures, algorithms)
- Machine learning infrastructure knowledge
- Project creation and maintenance
- System optimization
- Scalability and performance engineering
- Cross-team collaboration
Benefits
- Fortune recognized Best Workplace in Bay Area
- Certified Great Place to Work (2021-2024)
- Competitive total compensation package
- Work on state-of-the-art ML infrastructure
- Exposure to full ML pipeline
- Globally distributed platform experience
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AppLovin
AppLovin builds a large-scale advertising platform that processes billions of requests daily through distributed systems and machine learning-powered bidding infrastructure. The company is hiring backend engineers, ML infrastructure engineers, and partner solutions engineers to develop and maintain high-performance systems, optimize bidding ecosystems, and support strategic advertising integrations.
- Website
- applovin.com
Likely interview questions
- Walk us through your experience designing or maintaining a distributed system. What were the key challenges around scalability and latency?
- Describe a project where you optimized performance of a system or pipeline. What metrics did you improve and how did you approach it?