AI Researcher — Training Optimization

📋 Description

• Design and evaluate training optimization techniques for large models (optimizers, schedulers)
• Improve training efficiency and stability across long runs and large datasets
• Research and implement optimizer and scheduler innovations
• Mixed-precision, low-precision, and memory-efficient training
• Gradient noise reduction, scaling laws, convergence analysis
• Run large-scale experiments, analyze results, translate into improvements

🎯 Requirements

• Strong background in ML research, focusing on training dynamics and optimization
• Experience training large neural networks (LLMs, multimodal, or large seq models)
• Publication experience in ML venues (NeurIPS, ICML, ICLR, ACL, arXiv) or open research
• Proficiency in Python and modern ML frameworks (PyTorch preferred)
• Solid understanding of optimization theory and practice
• Distributed and large-batch training

🎁 Benefits

• Real influence over core model training decisions
• Freedom to pursue and publish novel research
• Direct access to large-scale experiments and production constraints
• Small, senior team that values deep thinking and shipping thoughtfully