Featherless AI
More roles at Featherless AIAI Researcher — Training Optimization
Remote work allowed only within certain countries or regions.
Employer listed it 9 months ago · Added 5 weeks ago
Been open since 9 months ago. Long-running listings are sometimes left up after the role is filled.
Salary
Not stated
Location
Timezone
Not stated
Contract
Full-time
Experience
Mid
Category
Other
The employer did not publish a salary, and we have too few comparable roles to estimate one.
What the advert says
Quoted word for word from the employer's advert. Where it says nothing, we say so.
- Working hours
- Not mentioned in the advert
- Where you can work from
RNN variants, long-context models, hybrid systems)
- Contract
- Not mentioned in the advert
- Experience asked for
- Not mentioned in the advert
- Pay
- Not mentioned in the advert
Remote flexibility
Region Restricted
Remote work is allowed, but the listing limits candidates to Remote (world).
What the employer says
- Source listing states candidate location: "Remote (world)"
What Nomaders makes of it
- Nomaders could not match this to a wider region
- Check the original posting
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
Before you apply
Experience: employer states
Written as a nice-to-have
- “Proficiency in Python and modern ML frameworks (PyTorch preferred)”
Questions worth asking: Nomaders interpretation
- No pay is stated. Ask for the salary range before investing time in interviews.
- The advert does not say whether this is employment or a contractor arrangement. Ask, as it affects tax and benefits.
Produced automatically from this advert's text; not reviewed by a person. The employer's own page is the final word.
About the role
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
Requirements
The employer hasn't listed requirements separately, they're described in the role summary above and on the original listing.
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, region restricted, matches where you plan to live and work.
- 2Tailor your CV to the role at Featherless AI, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 1mo ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
Pay not stated · You'll be taken to the employer's careers page.