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

- **Company:** Nunchaku AI, Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Visual Systems, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Large Language Models, Snowflake, Model Validation, Generative AI, Adobe, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_927006a51a6d9d00984a18309006dabf

## About the Role

* 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., * Experience distilling or fine-tuning large-scale diffusion or video-generation models.
 * Experience building automated judges or reward models for visual content, based on multimodal LLMs or vision-language models.
 * Experience with large-scale evaluation datasets, annotation pipelines, or preference data.
 * Experience building agentic visual systems.
 * Contributions to major open-source ML projects.

## Description

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.

 * Build data pipelines: Curate and version the training and benchmark data used for post-training and model evaluation.
 * Build evaluation systems: Create benchmarks and automated judges, and run human preference studies, to measure generation quality, fidelity, and efficiency.
 * Benchmark and release models: Measure models against relevant baselines, catch quality regressions, and give clear evidence for release decisions.
 * Bring research into production: Keep up with post-training and evaluation research, then integrate the useful methods into the team's pipelines., * Core technical work: Build the systems that let Nunchux make models faster without losing the quality customers care about.
 * From research to product: Your work will inform model releases, support customization, and run in production rather than stay in a notebook.
 * Technical collaboration: Work closely with a CMU PhD on our post-training work, and with the Nunchux research and inference teams.
 * Proven traction: Build on open-source work with more than 4 million model downloads, and on growing industry partnerships.
 * The team: Work with world-class researchers from MIT, Berkeley, and CMU, and with industry veterans from NVIDIA, AMD, Snowflake, and Adobe.