AI Researcher - Training Optimization

About the Role

We’re looking for an AI Researcher focused on training optimization to help us push the efficiency, stability, and scalability of large-scale model training. You’ll work at the intersection of research and systems, developing novel techniques to reduce training cost, accelerate convergence, and improve model quality - while validating ideas through rigorous experiments and publications. This role is ideal for someone who enjoys turning research insights into practical training wins, and who has a track record (or strong ambition) of publishing applied ML research.

What You’ll Work On - Design and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies) - Improve training efficiency and stability across long runs and large datasets - Research and implement methods such as: - Optimizer and scheduler innovations - Mixed-precision, low-precision, and memory-efficient training - Gradient noise reduction, scaling laws, and convergence analysis - Training-time regularization and robustness techniques - Run large-scale experiments, analyze results, and translate findings into actionable improvements - Author or co-author research papers, technical reports, or blog posts - Collaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance What We’re Looking For - Strong background in machine learning research, with emphasis on training dynamics and optimization - Experience training large neural networks (LLMs, multimodal models, or large sequence models) - Publication experience in ML venues (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv) or equivalent high-quality open research - Solid understanding of: - Optimization theory and practice - Backpropagation, gradient flow, and training stability - Distributed and large-batch training - Proficiency in Python and modern ML frameworks (PyTorch preferred) - Ability to independently design experiments and reason from data Nice to Have - Experience with non-standard architectures (e.g. RNN variants, long-context models, hybrid systems) - Experience optimizing training on GPUs at scale (FSDP, ZeRO, custom kernels) - Contributions to open-source ML or research codebases - Comfort operating in fast-moving, ambiguous startup environments Why This Role - Real influence over core model training decisions - Freedom to pursue and publish novel research - Direct access to large-scale experiments and real production constraints - A small, senior team that values thinking deeply and shipping thoughtfully Apply directly on RemoteJobs.org: https://remotejobs.org/remote-jobs/ai-researcher-training-optimization-featherless-ai