AI Researcher — Training Optimization
At a glance
Mid-level AI Research role at Featherless AI. Remote · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Create and test ways to make large-model training cheaper, faster to converge and more reliable
- Improve training behavior over extended runs and large datasets
- Experiment with precision, memory use, gradient noise, scaling and regularization methods
- Analyze large-scale experiment results and turn them into concrete training changes
- Write research papers, technical reports or blog posts, and coordinate with infrastructure and inference teams
What you bring
- A strong machine learning research background focused on optimization and how models train
- Hands-on experience training large neural networks, such as language or multimodal models
- Published ML research or equivalent high-quality open research
- Knowledge of optimization, gradient flow, training stability and distributed large-batch training
- Python proficiency and experience with modern ML frameworks
Who this fits
This role suits a researcher who can independently design experiments, interpret results and turn findings into practical training improvements. The position is remote and centers on individual research contributions alongside close work with infrastructure and inference teams.
From the employer
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