Machine Learning Engineer — Distillation
Featherless AI · Remote (world)
Extracted from the posting
Machine Learning Engineer building knowledge distillation pipelines, including teacher-student and multi-teacher methods, to create smaller, faster models from large foundation models. Work includes large-scale experiments on quality, latency, and cost trade-offs, plus evaluation tooling. Stack includes PyTorch or JAX and multi-GPU distributed setups.
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
Job details
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