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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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