{"id":694765,"url":"https://alion.io/job/featherless-ai-researcher-distillation","title":"AI Researcher — Distillation","company":{"id":673367,"name":"Featherless","domain":"featherless.ai","url":"https://alion.io/company/featherless","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":60,"open_postings":16,"ghost_share":0.75,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"arXiv","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-01-23T02:29:26Z","employer_posted_date":"2026-01-23","last_verified_at":"2026-11-02T22:45:39Z","board_verified":true,"closed_at":null,"days_open":283,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":283},"description":"About the Role\nWe’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.\nThis role is ideal for someone who enjoys publishing research, working close to real systems, and seeing their ideas move from papers → code → production.\nWhat You’ll Work On\nDesign and evaluate model distillation techniques (teacher-student training, self-distillation, layer-wise distillation, representation matching, etc.)\n\nResearch tradeoffs between model size, latency, memory, and accuracy\n\nDevelop novel distillation approaches for:\nLarge language models\n\nLong-context or specialized architectures\n\nInference-constrained environments\n\nRun large-scale experiments and ablations; analyze results rigorously\n\nCollaborate with engineers to productionize research outcomes\n\nWrite and submit research papers to top-tier venues (NeurIPS, ICML, ICLR, COLM, etc.)\n\nContribute to internal research notes, technical blogs, and open-source projects when appropriate\n\nWhat We’re Looking For\nRequired\nStrong background in machine learning research\n\nHands-on experience with model distillation or closely related topics (compression, pruning, quantization, representation learning)\n\nPublication experience (conference or journal papers, workshop papers, or arXiv preprints)\n\nSolid understanding of deep learning fundamentals (optimization, training dynamics, generalization)\n\nFluency in PyTorch (or equivalent) and research-grade experimentation\n\nAbility to clearly communicate research ideas, results, and limitations\n\nNice to Have\nExperience distilling large language models\n\nWork on efficiency-focused research (latency, memory, throughput)\n\nExperience with long-context models or non-Transformer architectures\n\nOpen-source contributions in ML or research tooling\n\nPrior startup or applied research experience\n\nWhy Join Us\nReal ownership over research direction at a Series A stage\n\nStrong support for 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