# Machine Learning Engineer, Visual Generative Models (Post-Training & Evaluation) at Nunchux

- Company: Nunchux
- What the company does: We build full-stack infrastructure that pushes generative media past the limits of today's hardware.

We help developers and enterprises run image, video, and world models up to 100× faster and cheaper, at the same fidelity. Backed by Emergence Capital.
- Company website: https://nunchux.ai
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco Office
- Work setup: Remote
- Pay: $180K to $250K base salary per year (USD)
- Posted: 2026-09-05
- Apply by: 2026-10-20
- Apply: https://jobs.ashbyhq.com/nunchux/0398a75c-75fe-427a-8d1b-dd0e70b830ea
- Page: https://www.1752.vc/careers/jobs/nunchux-machine-learning-engineer-visual-generative-models-post-training-and-eva/

## About the role

Nunchux makes visual generative models fast and efficient enough for production. As a Machine Learning Engineer on post-training and evaluation, you will build post-training pipelines that improve model efficiency and quality, measure the trade-offs, and turn the best recipes into reliable workflows for the models we ship. Develop post-training recipes: Establish and validate post-training recipes for image and video generation models.

## What they're looking for

- Visual generative-model experience: Hands-on experience with image or video generation models, or with 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: Depth in one of two areas: post-training methods such as distillation or LoRA, or the evaluation of visual generative models
- ML engineering strength: Strong Python and PyTorch skills, with experience building post-training or evaluation code that others can run and maintain
- Training systems: Comfortable running and adapting post-training workloads across multiple GPUs with FSDP, DeepSpeed, or similar tools
- Experimental judgment: Able to design clean experiments, interpret the results, and make practical recommendations from the data

Tags: Engineer