LikeRemoteAI Researcher — Training Optimization
from 🌏 Worldwide
About the Role
We’re looking for anAI 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 ofresearch 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 turningresearch insights into practical training wins, and who has a track record (or strong ambition) ofpublishing applied ML research.
What You’ll Work On
Design and evaluatetraining optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies)
Improvetraining 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-authorresearch 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 inmachine learning research, with emphasis ontraining dynamics and optimization
Experience traininglarge 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 inPython and modern ML frameworks (PyTorch preferred)
Ability to independently design experiments and reason from data
Nice to Have
Experience withnon-standard architectures (e.g. RNN variants, long-context models, hybrid systems)
Experience optimizing training on GPUs at scale (FSDP, ZeRO, custom kernels)
Contributions toopen-source ML or research codebases
Comfort operating in fast-moving, ambiguous startup environments
Why This Role
Real influence overcore model training decisions
Freedom to pursue and publishnovel research
Direct access to large-scale experiments and real production constraints
A small, senior team that valuesthinking deeply and shipping thoughtfully
