Machine Learning Engineer
Nunchux AI
Machine Learning Engineer
Nunchux AI
Full-stack infrastructure for generative media
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, and Zhekai Zhang with CMU Professor Jun-Yan Zhu, Nunchux brings together deep research expertise and production systems experience. Our work is built on nearly a decade of research from MIT and CMU, including nunchaku project, whose models have surpassed 4 million downloads. We have top VC backing, and we build for enterprises and for millions of developers.
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.
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.
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.
Compensation: $180,000 to $250,000 USD base salary, plus equity and comprehensive benefits that include health insurance and a 401(k). Actual compensation will depend on relevant experience, skills, and qualifications.
Location: San Francisco, CA. 4 days in office, 1 day remote.
Start date: As soon as available
Visa: We sponsor H-1B and other work visas for exceptional candidates.
Learn more: nunchux.ai
Apply: Please apply through our Ashby careers page.
Nunchux AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Full-stack infrastructure for generative media
What Nunchux AI does: It builds full-stack infrastructure that lets developers and enterprises run image, video, and world models much faster and cheaper while keeping the same level of fidelity. How its product works: The platform provides an end-to-end stack for deploying and running generative media models, including optimized runtimes, deployment tooling, and resource management, to squeeze more performance from hardware so workloads run up to 100× faster at the same quality. How it differs from competitors: It emphasizes large, concrete speed and cost improvements and offers a complete, full-stack solution for image, video, and world models rather than narrow tools, targeting both developers and enterprises. What its goal is: To push generative media beyond today’s hardware limits by making advanced models practical and affordable at scale.
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