AI Researcher — Training Optimization
Tasks
- Analyze experimental results
- Analyze scaling laws and convergence
- Apply training time regularization
- Collaborate with infrastructure teams
- Design training optimization techniques
- Evaluate training optimization techniques
- Implement mixed precision training
- Improve training efficiency and stability
- Publish research papers and technical reports
- Reduce gradient noise
- Research memory efficient training
- Research optimizer and scheduler innovations
- Run large-scale experiments
Perks/Benefits
- Access to large scale experiments
- Collaborative startup environment
- Freedom to pursue research
- Influence over model training decisions
- Research publication opportunity
Skills/Tech-stack
Backpropagation | Convergence analysis | Deep learning | Distributed Training | GPU Computing | Gradient Noise Reduction | Gradient descent | Machine Learning | Memory efficiency | Mixed Precision | Mixed-precision training | Neural Networks | Noise Reduction | Optimization | PyTorch | Python | Regularization | Scaling Laws
Education
N/A
Roles
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