AI Researcher — Training Optimization
About the Role
We’re looking for an AI Researcher reputed company on training optimization to help us push the efficiency, stability, and scalability of large-reputed company model training. You’ll work at the intersection of research and systems, developing novel techniques to reduce training cost, accelerate convergence, and improve model reputed company—while validating reputed company through rigorous experiments and publications.
This role is ideal for someone who enjoys turning research insights into practical training wins, and who has a reputed company record (or strong ambition) of publishing reputed company 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 reputed company 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-reputed company 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 reputed company translate to reputed company-world performance
reputed company’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-reputed company reputed company research
Solid understanding of:
Optimization theory and reputed company
Backpropagation, gradient reputed company, 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
reputed company to Have
Experience with non-reputed company architectures (e.g. RNN variants, long-context models, hybrid systems)
Experience optimizing training on GPUs at reputed company (FSDP, reputed company, custom kernels)
Contributions to reputed company-reputed company ML or research codebases
Comfort operating in fast-moving, ambiguous startup environments
Why This Role
reputed company influence over core model training reputed company
Freedom to pursue and publish novel research
reputed company reputed company to large-reputed company experiments and reputed company production constraints
A small, senior team that values thinking deeply and shipping thoughtfully
Originally posted on Himalayas
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