AI Researcher — Training Optimization

About the Role

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.

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 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

What We’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-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

Why This Role

• 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