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Reinforcement Learning
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Accountabilities:
- Design, implement, and evaluate novel training optimization techniques for large-scale neural networks.
- Investigate approaches for improving training efficiency, stability, convergence speed, and overall model quality.
- Collaborate with infrastructure and inference engineering teams to connect training decisions with real-world production performance.
Requirements:
- Strong background in machine learning research, with expertise in training dynamics, optimization, and large-scale model training.
- Strong proficiency in Python and experience with modern machine learning frameworks, particularly PyTorch.
- Ability to independently formulate research questions, design experiments, and interpret complex datasets.
Benefits:
- Full-time opportunity within a research-focused AI environment with remote working arrangement.
- Direct influence over core model training strategies and technical decisions.
- Small, senior-level team that values deep technical thinking and thoughtful execution.
Partner Company
This partner company focuses on AI research and training optimization for large-scale models. They maintain a small, senior-level team that values deep technical thinking and thoughtful execution.