[Remote] AI Researcher — Training Optimization

Note: The job is a remote job and is open to candidates in USA. FeatherlessAI is seeking an AI Researcher focused on training optimization to enhance the efficiency and scalability of large-scale model training. The role involves developing innovative techniques for training optimization and conducting rigorous experiments to validate findings. Responsibilities • 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 Skills • 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 • Solid understanding of backpropagation, gradient flow, and training stability • Solid understanding of distributed and large-batch training • Proficiency in Python and modern ML frameworks (PyTorch preferred) • Ability to independently design experiments and reason from data • 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 Company Overview • We enable serverless inference via our GPU orchestration and model load-balancing system. It was founded in 2023, and is headquartered in San Francisco, California, USA, with a workforce of 2-10 employees. Its website is Company H1B Sponsorship • Featherless AI has a track record of offering H1B sponsorships, with 1 in 2025. Please note that this does not guarantee sponsorship for this specific role. Apply tot his job