About Nunchux AI
Nunchux AI builds infrastructure that makes multimodal generative AI faster and cheaper to serve, and easier to build on. Founded by MIT PhDs Muyang Li, Yujun Lin, Zhekai Zhang, and CMU Professor Jun-Yan Zhu, Nunchux brings together deep research expertise and production systems experience. Built on nearly a decade of research from MIT and CMU, our work includes nunchaku, whose models have surpassed 4 million downloads. We’re backed by top VCs and building for enterprises and millions of developers.
The Role
Nunchux makes visual generative models fast and efficient enough for production. As an ML Engineer on Post-Training & Evaluation, you will build post-training pipelines that improve model efficiency and quality, evaluate the trade-offs, and turn the best recipes into reliable workflows for the models we ship.
What You’ll Do
Develop post-training recipes: Establish and validate post-training recipes for image and video generation models.
Build data pipelines: Build pipelines to curate and version training and benchmark data for post-training and model evaluation.
Build evaluation systems: Build benchmarks and automated judges, and run human preference studies to measure generation quality, fidelity, and efficiency.
Benchmark and release models: Benchmark models against relevant baselines, catch quality regressions, and provide clear evidence for release decisions.
Bring research into production: Keep up with post-training and evaluation research, then integrate useful methods into the team’s pipelines.
What You Bring
Visual generative-model experience: Hands-on experience working with image or video generation models, or other multimodal visual systems. You understand the artifacts, failure modes, and quality trade-offs that matter in generated visual content.
Post-training or evaluation depth: Strong experience in either post-training, such as distillation or LoRA, or in visual generative-model evaluation.
ML engineering strength: S...
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