AI Researcher — Distillation

About the RoleWe’re looking for an AI Researcher focused on model distillation to help us push the frontier of efficient, high-performance models. You’ll work on turning large, expensive models into smaller, faster, and more deployable systems—while maintaining or improving quality.This role is ideal for someone who enjoys publishing research, working close to real systems, and seeing their ideas move from papers → code → production.What You’ll Work OnDesign and evaluate model distillation techniques (teacher–student training, self-distillation, layer-wise distillation, representation matching, etc.)Research tradeoffs between model size, latency, memory, and accuracyDevelop novel distillation approaches for:Large language modelsLong-context or specialized architecturesInference-constrained environmentsRun large-scale experiments and ablations; analyze results rigorouslyCollaborate with engineers to productionize research outcomesWrite and submit research papers to top-tier venues (NeurIPS, ICML, ICLR, COLM, etc.)Contribute to internal research notes, technical blogs, and open-source projects when appropriateWhat We’re Looking ForRequiredStrong background in machine learning researchHands-on experience with model distillation or closely related topics (compression, pruning, quantization, representation learning)Publication experience (conference or journal papers, workshop papers, or arXiv preprints)Solid understanding of deep learning fundamentals (optimization, training dynamics, generalization)Fluency in PyTorch (or equivalent) and research-grade experimentationAbility to clearly communicate research ideas, results, and limitationsNice to HaveExperience distilling large language modelsWork on efficiency-focused research (latency, memory, throughput)Experience with long-context models or non-Transformer architecturesOpen-source contributions in ML or research toolingPrior startup or applied research experienceWhy Join UsReal ownership over research direction at a Series A stageStrong support for publishing and open researchTight feedback loop between research and real-world deploymentAccess to meaningful compute and production-scale problemsSmall, highly technical team with deep ML and systems expertiseExample BackgroundsML researchers from academia transitioning to industryResearch engineers with published work in model efficiencyPhD / Post-doc graduates or industry researchers who still want to publishOriginally posted on Himalayas