Machine Learning Engineer — Distillation

About the RoleWe’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 DoDesign and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)Distill large foundation models into smaller, faster, and cheaper models for inferenceRun and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffsCollaborate with research to translate new distillation ideas into production-ready codeOptimize 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 researchWhat We’re Looking ForStrong background in machine learning or deep learningHands-on experience with model distillation (LLMs or other neural networks)Solid understanding of training dynamics, loss functions, and optimizationExperience with PyTorch (or JAX) and modern ML toolingComfort running experiments on multi-GPU or distributed setupsAbility to reason about model quality vs. performance tradeoffsPragmatic mindset: you care about shipping, not just papersNice to HaveExperience distilling LLMs or large sequence modelsExperience with inference optimization (quantization, pruning, kernels, etc.)Familiarity with evaluation for language modelsOpen-source contributions or research publicationsExperience in early-stage or fast-moving startupsWhy JoinWork on core model quality and cost efficiency—not side projectsHigh ownership and direct impact on product and roadmapSmall, senior team with strong research + engineering cultureCompetitive compensation + meaningful equityRemote-friendly, async-first environmentOriginally posted on Himalayas