AI Researcher — Distillation

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

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

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

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