Lead Engineer, Machine Learning
Tasks
- Architect high performance inference systems
- Build data pipelines for real world and synthetic data
- Build training and fine tuning pipelines
- Collaborate with research and application engineering
- Deploy, monitor, and improve models in production
- Design evaluation systems
- Establish production ML infrastructure
- Iterate based on real world performance
- Optimize latency GPU utilization memory and cost
- Own end-to-end ML systems
Perks/Benefits
- N/A
Skills/Tech-stack
Cost Optimization | Data Pipelines | Fine Tuning | GPU Optimization | Inference Systems | JAX | Latency optimization | Machine Learning | Memory Optimization | Model Deployment | Model Evaluation | Model Fine-tuning | Model Monitoring | Production Engineering | PyTorch | Python | Training pipelines
Education
N/A
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