AI Researcher — Distillation

About the Role

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.

What You’ll

Work On

• 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

What We’re Looking For

Required

• 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

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

Why Join Us

• 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

Example Backgrounds

• 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