Featherlessai
Machine Learning Engineer — Distillation
5mo ago
pytorchjaxmachine learningdeep learningmodel distillationdistributed computingmulti-gpu
Engineer focused on building smaller, faster, and efficient machine learning models via model distillation, combining research with scalable production systems.
Responsibilities
- 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.
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
Conditions
- 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
Other
- 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
- 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