Machine Learning Engineer — Distillation

The role

You will design and implement knowledge distillation pipelines to build smaller, faster, and more efficient models. You will work at the intersection of research and production to ship models used in production.

What you will do

• Design and implement knowledge distillation pipelines
• 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
• Contribute to internal tooling, evaluation frameworks, and experiment tracking
• Contribute back to open-source models, tooling, or research

Required qualifications

• 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

Benefits

• Work on core model quality and cost efficiency
• 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

About the company

featherlessai is a small, senior team with a strong research + engineering culture.