AI Researcher — Training Optimization

Accountabilities:

• Design, implement, and evaluate novel training optimization techniques for large-scale neural networks, including optimization algorithms, schedulers, normalization methods, and curriculum strategies.
• Investigate approaches for improving training efficiency, stability, convergence speed, and overall model quality across long training runs and large datasets.
• Research and implement techniques involving optimizer and scheduler innovations, mixed- and low-precision training, memory-efficient training, gradient noise reduction, scaling laws, and convergence analysis.
• Explore training-time regularization and robustness techniques that can improve model performance and reliability.
• Design and execute large-scale experiments, analyze results rigorously, and translate findings into actionable improvements to training systems and methodologies.
• Author or co-author research papers, technical reports, blog posts, and other forms of high-quality technical communication.
• Collaborate with infrastructure and inference engineering teams to connect training decisions with real-world production performance and constraints.
• Independently investigate emerging research directions and contribute to core model training decisions.

Requirements:

• Strong background in machine learning research, with particular expertise in training dynamics, optimization, and large-scale model training.
• Demonstrated experience training large neural networks, including large language models, multimodal models, or other large sequence models.
• Publication experience at machine learning or natural language processing venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv, or through equivalent high-quality open research.
• Strong understanding of optimization theory and practice, backpropagation, gradient flow, training stability, distributed training, and large-batch training.
• Strong proficiency in Python and experience with modern machine learning frameworks, particularly PyTorch.
• Ability to independently formulate research questions, design experiments, interpret complex datasets, and reason from empirical evidence.
• Experience with non-standard architectures, such as RNN variants, long-context models, or hybrid systems, is a plus.
• Experience optimizing large-scale GPU training using technologies such as FSDP, ZeRO, or custom kernels is desirable.
• Contributions to open-source machine learning projects or research codebases are advantageous.
• Ability to work effectively in a fast-moving, ambiguous environment while maintaining strong scientific rigor and technical quality.
• Strong communication and collaboration skills, with the ability to explain complex research findings and work effectively across research and engineering teams.

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, and model quality.
• Close collaboration with infrastructure and inference engineering teams.
• Opportunity to contribute to research papers, technical reports, and open technical work.
• Small, senior-level team that values deep technical thinking and thoughtful execution.