Featherless
via Ashby
Machine Learning Engineer — AI Architecture Research
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 10h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 235d ago
The date the source published, not the day we noticed it (2026-01-22). Last seen at its source just now.
Is it remote?
Remote (world)
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Available worldwide
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $110.6k–163.4k/yr
Middle 50% of 26 listings that do state pay — Engineering · all levels · Worldwide · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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ashby employer's own board first seen 38d ago · last seen just now
The listing
About the Role
We’re looking for a Machine Learning Engineer focused on AI architecture research to help design, prototype, and validate next-generation model architectures. You’ll work at the intersection of research and production — turning new ideas into scalable, real-world systems.
This role is ideal for someone who enjoys questioning architectural assumptions, experimenting with novel model designs, and pushing beyond standard Transformer-style approaches.
What You’ll Work On
Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems)
Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs)
Prototype models end-to-end — from research code to training-ready implementations
Collaborate with inference and systems engineers to ensure architectures are deployable and efficient
Analyze model behavior, failure modes, and inductive biases
Read, reproduce, and extend cutting-edge research papers
Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)
What We’re Looking For
Strong background in machine learning fundamentals and deep learning
Hands-on experience implementing model architectures from scratch
Solid understanding of:
Attention mechanisms, RNNs, state-space models, or hybrid architectures
Training dynamics, scaling behavior, and optimization
Memory, latency, and compute constraints at the model level
Comfortable working in PyTorch or JAX
Ability to move fluidly between theory, experimentation, and engineering
Clear communicator who can explain architectural trade-offs
Nice to Have
Experience with non-Transformer architectures (RNN variants, SSMs, long-context models)
Background in research-driven startups or open-source ML projects
Experience with large-scale training or custom training loops
Publications, preprints, or notable research contributions
Familiarity with inference optimization and deployment constraints
Why Join
Work on core model architecture, not just fine-tuning
Direct influence on the technical direction of a Series-A company
Small, high-caliber team with fast feedback loops
Opportunity to ship research into production
Competitive compensation + meaningful equity