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# Machine Learning Engineer, Post-Training & Evaluation

- **Company:** Nunchaku AI, Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $180,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Visual Systems, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Snowflake, Model Validation, Adobe, Machine Learning Operations, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=729ddecade3b10f2

## 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.

Bonus Points

 * 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.