Featherlessai
AI Researcher — Training Optimization
5mo ago
machine learningoptimization algorithmstraining efficiencyscaling lawsmixed-precision training
AI Researcher focused on training optimization to improve efficiency, stability, and scalability of large-scale model training.
Responsibilities
- 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 .
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
Other
- 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
- 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
- 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