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