AI Researcher — Distillation

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.

• 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)

• 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

• 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