AI Researcher — Training Optimization

📋 Description

• Design, implement, and evaluate novel training optimization techniques for large-scale neural
• Investigate approaches for improving training efficiency, stability, convergence speed, and overall
• Research and implement techniques involving optimizer and scheduler innovations, mixed- and
• Explore training-time regularization and robustness techniques that can improve model performance
• Design and execute large-scale experiments, analyze results rigorously, and translate findings into
• Author or co-author research papers, technical reports, blog posts, and other forms of high-quality

🎯 Requirements

• Strong background in machine learning research, with particular expertise in training dynamics
• Demonstrated experience training large neural networks, including large language models, multimodal
• Publication experience at machine learning or natural language processing venues such as NeurIPS
• Strong understanding of optimization theory and practice, backpropagation, gradient flow, training
• Strong proficiency in Python and experience with modern machine learning frameworks, particularly
• Ability to independently formulate research questions, design experiments, interpret complex

🎁 Benefits

• Full-time opportunity within a research-focused AI environment.
• Remote working arrangement with the flexibility to contribute from locations worldwide.
• Direct influence over core model training strategies and technical decisions.
• Freedom to investigate novel research ideas and publish meaningful research.
• Direct access to large-scale experiments and real-world production constraints.
• Opportunity to work on advanced problems involving training optimization, efficiency, convergence