Description review
AI Researcher — Training Optimization
Featherless AI · Remote · back to the listing
HR standards
58/100
needs work
Title ↔ description
78/100
solid
Reads as
Machine Learning Engineer
87% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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Startups
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What the listing never says
- 27 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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
Originally posted on Himalayas
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
Originally posted on Himalayas