Featherlessai
AI Researcher — Distillation
5mo ago
machine learningmodel distillationresearchlarge language modelsquantizationpruning
AI Researcher focused on model distillation to develop efficient and deployable models from large, complex systems.
Responsibilities
- We’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.
Nice to have
- Experience distilling 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
Conditions
- Real ownership over research direction at a Series A stage
- Strong support for publishing and open research
- Tight feedback loop between research and real-world deployment
- Access to meaningful compute and production-scale problems
- Small, highly technical team with deep ML and systems expertise
Other
- Design and evaluate model distillation techniques (teacher–student training, self-distillation, layer-wise distillation, representation matching, etc.)
- Research tradeoffs between model size, latency, memory, and accuracy
- Develop novel distillation approaches for: Large language models
- Long-context or specialized architectures
- 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
- Strong background in machine learning research
- Hands-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 experimentation
- Ability to clearly communicate research ideas, results, and limitations
- ML researchers from academia transitioning to industry
- Research engineers with published work in model efficiency
- PhD / Post-doc graduates or industry researchers who still want to publish