Serverless AI Inference - run any model, at any scale, without managing GPUs
AI Researcher – Training Optimization
Location
Worldwide
Posted
133 days ago
Salary
0
Seniority
Senior
Job Description
AI Researcher – Training Optimization
Featherless AI
• 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
Job Requirements
- 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
Benefits
- Freedom to pursue and publish novel research
- Real influence over core model training decisions
- Direct access to large-scale experiments and real production constraints
- A small, senior team that values thinking deeply and shipping thoughtfully
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