Machine Learning Engineer — Distillation
At a glance
Mid-level Machine Learning Engineering role at Featherless AI. Remote · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build distillation pipelines and use them to make large foundation models smaller and more economical at inference
- Run training trials at scale, then compare model quality, response speed and operating cost
- Turn new distillation approaches from research into code that can run in production
- Improve training and inference efficiency across memory use, throughput and latency
- Add to internal tools for evaluation and experiment tracking
What you bring
- A strong grounding in machine learning or deep learning, with practical model-distillation experience
- A solid grasp of how training behaves, how loss functions work and how optimization affects models
- Experience with PyTorch or JAX, modern ML tools and multi-GPU or distributed experiments
- You can weigh model quality against performance and favor work that reaches production
Who this fits
This suits a hands-on, mid-level engineer who wants ownership across research and production. The role is remote-friendly and async-first.
From the employer
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