Optimizing Large-Scale Recommendation Systems
7 days ago
The MRS ML Infra team focuses on optimizing performance and efficiency for AI training and inference workflows in large-scale recommendation domains. In this role, you will work on enhancing the e2e stack for model training and inference for large-scale recommendation models.
This involves identifying and executing short- and mid-term opportunities for efficiency optimization while driving long-term strategies and shaping team direction on matters like model/system co-design, performance automation, regression detection, and mitigation.
Main Responsibilities- Identify performance bottlenecks across a wide range of MRS models, infrastructure, and systems
- Implement changes to capture efficiency improvements
- Guide other engineers in executing on efficiency and performance opportunities
- Cross-functional collaborations with partner or product ML teams
To be successful in this role, you need to have 6+ years of programming experience in relevant coding languages, 6+ years of relevant experience building large-scale infrastructure applications, and experience designing, analyzing, and improving efficiency, scalability, and stability of system resources.
A bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience, is required.
About MetaMeta builds technologies that help people connect, find communities, and grow businesses. We are moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology.
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