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