Das ist der Job
Focus on improving developer productivity, reducing costs, and accelerating experimentation.
Darum lohnt es sich
The ML Efficiency team builds infrastructure and tooling for efficient ML training and inference at scale. 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. #J-18808-Ljbffr
