Featherless
via Ashby
Machine Learning Engineer — Distillation
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 1d 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 236d 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
-
ashby employer's own board first seen 39d ago · last seen just now
The listing
About the Role
We’re looking for a Machine Learning Engineer focused on model distillation to help us build smaller, faster, and more efficient models without sacrificing quality. You’ll work at the intersection of research and production—taking cutting-edge techniques and turning them into systems that scale.
This is a hands-on role with real ownership: you’ll design distillation pipelines, run large-scale experiments, and ship models used in production.
What You’ll Do
Design and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)
Distill large foundation models into smaller, faster, and cheaper models for inference
Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs
Collaborate with research to translate new distillation ideas into production-ready code
Optimize training and inference performance (memory, throughput, latency)
Contribute to internal tooling, evaluation frameworks, and experiment tracking
(Optional) Contribute back to open-source models, tooling, or research
What We’re Looking For
Strong background in machine learning or deep learning
Hands-on experience with model distillation (LLMs or other neural networks)
Solid understanding of training dynamics, loss functions, and optimization
Experience with PyTorch (or JAX) and modern ML tooling
Comfort running experiments on multi-GPU or distributed setups
Ability to reason about model quality vs. performance tradeoffs
Pragmatic mindset: you care about shipping, not just papers
Nice to Have
Experience distilling LLMs or large sequence models
Experience with inference optimization (quantization, pruning, kernels, etc.)
Familiarity with evaluation for language models
Open-source contributions or research publications
Experience in early-stage or fast-moving startups
Why Join
Work on core model quality and cost efficiency—not side projects
High ownership and direct impact on product and roadmap
Small, senior team with strong research + engineering culture
Competitive compensation + meaningful equity
Remote-friendly, async-first environment