The RoleNunchux 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.
What You'll Do- 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.
What You Bring- 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.
Why Join- 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.
