Featherless AI - Machine Learning Engineer — Distillation
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Requirements
• 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 • 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
Responsibilities
• 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
Benefits
• 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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