Machine Learning Engineer — Distillation at Featherless AI
Featherless AI is hiring a Machine Learning Engineer — Distillation in Remote. Remote.
About Featherless AI
Featherless AI provides a serverless LLM hosting platform that lets users deploy and access open models through a single API key, offering a catalog of 40,000+ models. The company also builds an inference stack for specialized intelligence as a venture-backed AI inference provider.
Machine Learning Engineer — Distillation job description
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
Apply now
Applications go straight to Featherless AI. We never sit between you and the employer.
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