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Performance Optimization
About the Team:
- The ML Efficiency team builds infrastructure and tooling for efficient ML training and inference at scale.
- Focus on improving developer productivity, reducing costs, and accelerating experimentation.
Responsibilities:
- Design and build systems to improve ML training and inference efficiency.
- Develop tooling for debugging, profiling, and monitoring model performance.
- Optimize distributed training infrastructure and model serving architectures.
What Success Looks Like:
- ML engineers move from idea to experiment faster with reduced training costs.
- GPU utilization increases and platform reliability improves as workloads scale.
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