AI Researcher — Training Optimization
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Before you apply
LocationRemote (world)
Work moderemote · United States
Typefull-time
SenioritySenior
DepartmentResearch
Company size51–200 people
PostedJan 23, 2026 · 9mo ago
Verified live4m ago
May no longer be actively hiring
Posted by the company on Jan 23, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
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
Solid understanding of backpropagation, gradient flow, and training stability
Solid understanding of distributed and large-batch training
Proficiency in Python and modern ML frameworks (PyTorch preferred)
Ability to independently design experiments and reason from data
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
Skills
Python
PyTorch
FSDP
ZeRO
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
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Design and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies)
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Improve training efficiency and stability across long runs and large datasets
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Research and implement methods such as:
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Optimizer and scheduler innovations
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Mixed-precision, low-precision, and memory-efficient training
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Gradient noise reduction, scaling laws, and convergence analysis
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Training-time regularization and robustness techniques
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Run large-scale experiments, analyze results, and translate findings into actionable improvements
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Author or co-author research papers, technical reports, or blog posts
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Collaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance
What We’re Looking For
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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
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Real influence over core model training decisions
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Freedom to pursue and publish novel research
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Direct access to large-scale experiments and real production constraints
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A small, senior team that values thinking deeply and shipping thoughtfully