Machine Learning Engineer — Distillation

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 Originally posted on Himalayas