featherlessai - AI Researcher — Distillation
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Requirements
• Strong background in machine learning research • machine learning research • Hands-on experience with model distillation or closely related topics (compression, pruning, quantization, representation learning) • model distillation • 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 experimentation • PyTorch • Ability to clearly communicate research ideas, results, and limitations • Experience distilling large language models • large language models • Work on efficiency-focused research (latency, memory, throughput) • Experience with long-context models or non-Transformer architectures • Open-source contributions in ML or research tooling • Prior startup or applied research experience
Responsibilities
• Design and evaluate model distillation techniques. • Research tradeoffs between model size, latency, memory, and accuracy. • Develop novel distillation approaches for large language models, long-context architectures, and inference-constrained environments. • Run large-scale experiments and ablations; analyze results rigorously. • Collaborate with engineers to productionize research outcomes. • Write 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 appropriate.
Benefits
• Equity options mentioned as part of the role's benefits. • Perks such as access to meaningful compute resources or production-scale problems could potentially fall under this category but are not directly mentioned in bullet points; they can be inferred from the job description's emphasis on a tight feedback loop between research and real-world deployment, which often comes with additional perks. • Remote work options are explicitly stated as part of what is "Nice to Have."
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