{"id":3425497,"url":"https://alion.io/job/featherless-ai-machine-learning-engineer-distillation","title":"Machine Learning Engineer — Distillation","company":{"id":4483944,"name":"Featherless AI","domain":null,"url":"https://alion.io/company/featherless-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"worldwide","remote_scope_basis":"board_field","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":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":true}],"status":"live","first_seen_at":"2026-09-24T06:31:41Z","employer_posted_date":null,"last_verified_at":"2026-09-24T06:31:41Z","board_verified":false,"closed_at":null,"days_open":35,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":35},"description":"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.\nThis is a hands-on role with real ownership: you’ll design distillation pipelines, run large-scale experiments, and ship models used in production.\nWhat You’ll Do\nDesign and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)\n\nDistill large foundation models into smaller, faster, and cheaper models for inference\n\nRun and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs\n\nCollaborate with research to translate new distillation ideas into production-ready code\n\nOptimize training and inference performance (memory, throughput, latency)\n\nContribute to internal tooling, evaluation frameworks, and experiment tracking\n\n(Optional) Contribute back to open-source models, tooling, or research\n\nWhat We’re Looking For\nStrong background in machine learning or deep 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