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
remoteroles summary, based on the employer's posting.
What you'll do
- Build distillation pipelines that transfer knowledge from teacher models, multiple teachers or a model to itself
- Compress large foundation models into smaller versions that run faster and cost less to serve
- Run training experiments and compare model quality, response speed and inference cost
- Turn research ideas into production code, then improve training and inference for memory, throughput and latency
- Develop internal tools for evaluation and experiment tracking, with optional open-source contributions
What you bring
- You have a strong grounding in machine learning or deep learning and practical experience distilling neural networks
- You understand training behavior, loss functions and optimization well enough to guide model development
- You have used PyTorch or JAX and modern machine-learning tools, including multi-GPU or distributed computing
Who this fits
This hands-on, mid-level role suits an engineer who wants to move between research experiments and production systems. It is remote-friendly and async-first, with ownership over work that reaches production.
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
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