AI Training Optimization Scientist: Scale Efficiently

Featherless AI; United States; Remote; USD 140,000 - 230,000; Full time

Job summary

United States Digital Space LLC is seeking an AI Researcher focused on training optimization to push efficiency and scalability of large-scale model training. You will work at the intersection of research and systems to develop techniques to reduce training cost and accelerate convergence.

Ideal candidates have a track record (or strong ambition) of publishing applied ML research and will collaborate with infrastructure and inference teams to translate research into real-world performance.

Qualifications

• Strong background in ML research with emphasis on training dynamics and optimization.
• Experience training large neural networks (LLMs, multimodal models, or long sequence models).
• Publication experience in ML venues or equivalent high-quality open research.
• Proficiency in Python and modern ML frameworks (PyTorch preferred).

Responsibilities

• Design and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies).
• Run large-scale experiments, analyze results, and translate findings into actionable improvements.
• Collaborate with infrastructure and inference teams to ensure training decisions translate to real-world performance.
• Author or co-author research papers, technical reports, or blog posts.

Skills

Training optimization

Large models

Python

Experiment design

Distributed training

Publications

Tools

PyTorch

Job description

United States Digital Space LLC is seeking an AI Researcher focused on training optimization to push efficiency and scalability of large-scale model training. You will work at the intersection of research and systems to develop techniques to reduce training cost and accelerate convergence.

Ideal candidates have a track record (or strong ambition) of publishing applied ML research and will collaborate with infrastructure and inference teams to translate research into real-world performance.