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 Modelverse is our live model hub and API platform for multimodal generative AI. We continue to add models, features, and developer workflows to it.
As a Product Engineer, you will turn Nunchux’s model roadmap into product experiences across Modelverse, the Playground, the developer dashboard, the API, the SDK, and the documentation. You will work with founders, designers, and other engineers to take a new model or feature from a product direction to a reliable release. You will also use product data and developer feedback to find friction and improve what is already live.
Build and launch: Turn new models and features into production-ready experiences across the model catalog, the Playground, the dashboard, the API, the SDK, the documentation, the frontend, the backend, the database, and the deployment.
Improve the experience: Use product data, developer feedback, and technical constraints to find improvements to model discovery, onboarding, and the core content-generation workflows. Work with the founders and the designers to scope and ship them.
Learn from launches: Instrument the key flows, find where developers hit errors or drop off, and make changes after launch.
Stay close to developers: Work with the founders and with customer-facing teammates to learn which developer questions and problems occur again and again. Help users make their first API call, and write the technical explainers or demos that go with major releases.
Move quickly: Build small demo apps, client libraries, comparison tools, or landing pages to test an idea before you commit to a larger build.
Full-stack experience: 4+ years of building and shipping production web products. You have made product decisions on a small team, not just implemented someone else's specification.
Technical range: Strong in TypeScript/JavaScript and Python. Comfortable owning a Next.js frontend as well as the API and database layer behind it, including Postgres and schema design.
Product judgment: Able to look at usage data, support threads, and customer feedback, then find a problem and recommend a practical way to fix it.
Developer empathy and writing: Comfortable debugging a developer's first experience and writing clear docs, specs, or technical posts.
Working style: Comfortable with unclear requirements and able to move from a live demo or a customer conversation to a production pull request without a large process around you.
Experience at a developer-tools company, an API platform, or a product-led-growth startup.
A track record of launching a 0-to-1 feature or product at an early-stage company.
Open-source projects, technical writing, or side projects that show you can both build and explain.
Background in ML, systems, or applied research. You understand what model optimization means in practice.
Ownership: Turn Nunchux's model roadmap into the product surfaces that developers use, and help make each launch complete and coherent.
A live platform: Work on real user problems, new model launches, and core developer workflows.
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: $170,000 to $240,000 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.
","descriptionPlain":"ABOUT NUNCHUX AI\n\nNunchux AI https://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 https://github.com/nunchux-ai/nunchaku project, whose models have surpassed 4 million downloads. We have top VC backing, and we build for enterprises and for millions of developers.\n\n\n\n\nTHE ROLE\n\nNunchux Modelverse is our live model hub and API platform for multimodal generative AI. We continue to add models, features, and developer workflows to it.\n\nAs a Product Engineer, you will turn Nunchux’s model roadmap into product experiences across Modelverse, the Playground, the developer dashboard, the API, the SDK, and the documentation. You will work with founders, designers, and other engineers to take a new model or feature from a product direction to a reliable release. You will also use product data and developer feedback to find friction and improve what is already live.\n\n\n\n\nWHAT YOU’LL DO\n\n - Build and launch: Turn new models and features into production-ready experiences across the model catalog, the Playground, the dashboard, the API, the SDK, the documentation, the frontend, the backend, the database, and the deployment.\n\n - Improve the experience: Use product data, developer feedback, and technical constraints to find improvements to model discovery, onboarding, and the core content-generation workflows. Work with the founders and the designers to scope and ship them.\n\n - Learn from launches: Instrument the key flows, find where developers hit errors or drop off, and make changes after launch.\n\n - Stay close to developers: Work with the founders and with customer-facing teammates to learn which developer questions and problems occur again and again. Help users make their first API call, and write the technical explainers or demos that go with major releases.\n\n - Move quickly: Build small demo apps, client libraries, comparison tools, or landing pages to test an idea before you commit to a larger build.\n\n\nWHAT YOU BRING\n\n - Full-stack experience: 4+ years of building and shipping production web products. You have made product decisions on a small team, not just implemented someone else's specification.\n\n - Technical range: Strong in TypeScript/JavaScript and Python. Comfortable owning a Next.js frontend as well as the API and database layer behind it, including Postgres and schema design.\n\n - Product judgment: Able to look at usage data, support threads, and customer feedback, then find a problem and recommend a practical way to fix it.\n\n - Developer empathy and writing: Comfortable debugging a developer's first experience and writing clear docs, specs, or technical posts.\n\n - Working style: Comfortable with unclear requirements and able to move from a live demo or a customer conversation to a production pull request without a large process around you.\n\n\nBONUS POINTS\n\n - Experience at a developer-tools company, an API platform, or a product-led-growth startup.\n\n - A track record of launching a 0-to-1 feature or product at an early-stage company.\n\n - Open-source projects, technical writing, or side projects that show you can both build and explain.\n\n - Background in ML, systems, or applied research. You understand what model optimization means in practice.\n\n\nWHY JOIN\n\n - Ownership: Turn Nunchux's model roadmap into the product surfaces that developers use, and help make each launch complete and coherent.\n\n - A live platform: Work on real user problems, new model launches, and core developer workflows.\n\n - Proven traction: Build on open-source work with more than 4 million model downloads, and on growing industry partnerships.\n\n - The team: Work with world-class researchers from MIT, Berkeley, and CMU, and with industry veterans from NVIDIA, AMD, Snowflake, and Adobe.\n\n - Compensation: $170,000 to $240,000 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.\n\nLocation: San Francisco, CA. 4 days in office, 1 day remote. \n\nStart date: As soon as available. \n\nVisa: We sponsor H-1B and other work visas for exceptional candidates. \n\nLearn more: nunchux.ai http://nunchux.ai \n\nApply: Please apply through our Ashby careers page.\n\nNunchux AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.","compensation":{"compensationTierSummary":"$170K – $240K • Offers Equity","scrapeableCompensationSalarySummary":"$170K - $240K","compensationTiers":[{"id":"1bdbee7a-5f77-4c65-9913-fb4e526848b1","tierSummary":"Base $170K – $240K • Offers Equity","title":"All","additionalInformation":null,"components":[{"id":"6fa702dc-875b-42e2-b392-1118b8ef8c9e","summary":"Base $170K – $240K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":170000,"maxValue":240000},{"id":"43b04a09-b9dc-4b90-bddb-cdb921aa96b3","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":170000,"maxValue":240000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}},{"id":"0398a75c-75fe-427a-8d1b-dd0e70b830ea","title":"Machine Learning Engineer, Visual Generative Models (Post-Training & Evaluation)","department":"Engineer","team":"Engineer","employmentType":"FullTime","location":"San Francisco Office","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-09-05T03:34:34.849+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"postalCode":"94104","addressRegion":"California","streetAddress":"44 Montgomery Street","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/nunchux/0398a75c-75fe-427a-8d1b-dd0e70b830ea","applyUrl":"https://jobs.ashbyhq.com/nunchux/0398a75c-75fe-427a-8d1b-dd0e70b830ea/application","descriptionHtml":"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.
","descriptionPlain":"ABOUT NUNCHUX AI\n\nNunchux AI https://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 https://github.com/nunchux-ai/nunchaku project, whose models have surpassed 4 million downloads. We have top VC backing, and we build for enterprises and for millions of developers.\n\n \n\n\nTHE ROLE\n\nNunchux 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.\n\n\n\n\nWHAT YOU’LL DO\n\n - Develop post-training recipes: Establish and validate post-training recipes for image and video generation models.\n\n - Build data pipelines: Curate and version the training and benchmark data used for post-training and model evaluation.\n\n - Build evaluation systems: Create benchmarks and automated judges, and run human preference studies, to measure generation quality, fidelity, and efficiency.\n\n - Benchmark and release models: Measure models against relevant baselines, catch quality regressions, and give clear evidence for release decisions.\n\n - Bring research into production: Keep up with post-training and evaluation research, then integrate the useful methods into the team's pipelines.\n \n \n\n\nWHAT YOU BRING\n\n - 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.\n\n - 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.\n\n - ML engineering strength: Strong Python and PyTorch skills, with experience building post-training or evaluation code that others can run and maintain.\n\n - Training systems: Comfortable running and adapting post-training workloads across multiple GPUs with FSDP, DeepSpeed, or similar tools.\n\n - Experimental judgment: Able to design clean experiments, interpret the results, and make practical recommendations from the data.\n\n\nBONUS POINTS\n\n - Experience distilling or fine-tuning large-scale diffusion or video-generation models.\n\n - Experience building automated judges or reward models for visual content, based on multimodal LLMs or vision-language models.\n\n - Experience with large-scale evaluation datasets, annotation pipelines, or preference data.\n\n - Experience building agentic visual systems.\n\n - Contributions to major open-source ML projects.\n\n\nWHY JOIN\n\n - Core technical work: Build the systems that let Nunchux make models faster without losing the quality customers care about.\n\n - From research to product: Your work will inform model releases, support customization, and run in production rather than stay in a notebook.\n\n - Technical collaboration: Work closely with a CMU PhD on our post-training work, and with the Nunchux research and inference teams.\n\n - Proven traction: Build on open-source work with more than 4 million model downloads, and on growing industry partnerships.\n\n - The team: Work with world-class researchers from MIT, Berkeley, and CMU, and with industry veterans from NVIDIA, AMD, Snowflake, and Adobe.\n\n - 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.\n\nLocation: San Francisco, CA. 4 days in office, 1 day remote. \n\nStart date: As soon as available \n\nVisa: We sponsor H-1B and other work visas for exceptional candidates. \n\nLearn more: nunchux.ai http://nunchux.ai \n\nApply: Please apply through our Ashby careers page.\n\nNunchux AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.","compensation":{"compensationTierSummary":"$180K – $250K • Offers Equity","scrapeableCompensationSalarySummary":"$180K - $250K","compensationTiers":[{"id":"1960c1a9-0e82-4e30-a567-c75d8e5ba658","tierSummary":"$180K – $250K • Offers Equity","title":"All","additionalInformation":null,"components":[{"id":"66dcd52d-7f9a-4f4e-9d6b-5a7e519a6d7b","summary":"$180K – $250K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":180000,"maxValue":250000},{"id":"7dac77e6-184b-4469-8538-aa6ca232dd88","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":180000,"maxValue":250000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}},{"id":"7924452c-128d-42f2-90ef-b52123f5566b","title":"Machine Learning Engineer, Model Integrations","department":"Engineer","team":"Engineer","employmentType":"FullTime","location":"San Francisco Office","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-09-06T03:04:56.029+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"postalCode":"94104","addressRegion":"California","streetAddress":"44 Montgomery Street","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/nunchux/7924452c-128d-42f2-90ef-b52123f5566b","applyUrl":"https://jobs.ashbyhq.com/nunchux/7924452c-128d-42f2-90ef-b52123f5566b/application","descriptionHtml":"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.
Help Nunchux bring new image and video models to Modelverse as soon as they become available. Your focus is speed to launch: get the model running, connect it to the platform, and ship a working first version.
You will own the initial integration and the Day 1 release, working with the cloud team on deployment and with the product engineers on the API and Modelverse integration. After launch, you will hand off the further work on performance, cost, and model quality to the relevant teams. Between launches, you will maintain and extend our shared code for model integration and serving.
Prototype new models: Get new image and video models running quickly with the available code and weights. Run test examples and identify what each model needs for launch.
Ship the first version: Coordinate the deployment with the cloud team, and the API and Modelverse integration with the product engineers. Get the basic parameters, examples, and developer instructions ready for Day 1.
Speed up the launch process: Find bottlenecks, automate manual steps, and simplify the handoffs with the cloud and product teams. Build reusable adapters and launch scripts to reduce the work each new model takes.
Verify and hand off: Test the integration end to end, including the outputs and the error handling. Fix launch blockers, and document the known limitations for the teams that take on further optimization.
Maintain the integration platform: Between launches, fix bugs and add support for new model interfaces, providers, and modalities in our shared integration and serving code.
Image or video model experience: You have run and adapted generation models, or integrated provider APIs.
Python and PyTorch: Strong in both, and comfortable reading model code, adapting inference pipelines, and debugging model behavior.
Production systems experience: You have deployed a model or built an API integration. Comfortable working with existing serving tools, reading logs, and debugging failed requests.
Working style: Quick to learn unfamiliar model code and get a prototype working. Able to keep the first release focused, resolve launch blockers, and coordinate with teammates to ship.
Experience with Hugging Face, Diffusers, ComfyUI, or model-serving frameworks such as SGLang or vLLM.
Experience working with external model providers, or building a multi-model API platform.
Contributions to open-source ML or inference projects.
Own model launches: Take new models from their first run to a release developers can use on Modelverse.
Work with new models: Get hands-on with image and video models as they ship, across providers and architectures.
Proven traction: Build on open-source work with more than 4 million model downloads, and on growing industry partnerships.
The team: Work with researchers from MIT, Berkeley, and CMU, and with industry veterans from NVIDIA, AMD, Snowflake, and Adobe.
Compensation: $170,000 to $240,000 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.
","descriptionPlain":"ABOUT NUNCHUX AI\n\nNunchux AI https://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 https://github.com/nunchux-ai/nunchaku project, whose models have surpassed 4 million downloads. We have top VC backing, and we build for enterprises and for millions of developers.\n\n \n\n\nTHE ROLE\n\nHelp Nunchux bring new image and video models to Modelverse as soon as they become available. Your focus is speed to launch: get the model running, connect it to the platform, and ship a working first version.\n\nYou will own the initial integration and the Day 1 release, working with the cloud team on deployment and with the product engineers on the API and Modelverse integration. After launch, you will hand off the further work on performance, cost, and model quality to the relevant teams. Between launches, you will maintain and extend our shared code for model integration and serving.\n\n\n\n\nWHAT YOU’LL DO\n\n - Prototype new models: Get new image and video models running quickly with the available code and weights. Run test examples and identify what each model needs for launch.\n\n - Ship the first version: Coordinate the deployment with the cloud team, and the API and Modelverse integration with the product engineers. Get the basic parameters, examples, and developer instructions ready for Day 1.\n\n - Speed up the launch process: Find bottlenecks, automate manual steps, and simplify the handoffs with the cloud and product teams. Build reusable adapters and launch scripts to reduce the work each new model takes.\n\n - Verify and hand off: Test the integration end to end, including the outputs and the error handling. Fix launch blockers, and document the known limitations for the teams that take on further optimization.\n\n - Maintain the integration platform: Between launches, fix bugs and add support for new model interfaces, providers, and modalities in our shared integration and serving code.\n\n\nWHAT YOU BRING\n\n - Image or video model experience: You have run and adapted generation models, or integrated provider APIs.\n\n - Python and PyTorch: Strong in both, and comfortable reading model code, adapting inference pipelines, and debugging model behavior.\n\n - Production systems experience: You have deployed a model or built an API integration. Comfortable working with existing serving tools, reading logs, and debugging failed requests.\n\n - Working style: Quick to learn unfamiliar model code and get a prototype working. Able to keep the first release focused, resolve launch blockers, and coordinate with teammates to ship.\n\n\nBONUS POINTS\n\n - Experience with Hugging Face, Diffusers, ComfyUI, or model-serving frameworks such as SGLang or vLLM.\n\n - Experience working with external model providers, or building a multi-model API platform.\n\n - Contributions to open-source ML or inference projects.\n\n\nWHY JOIN\n\n - Own model launches: Take new models from their first run to a release developers can use on Modelverse.\n\n - Work with new models: Get hands-on with image and video models as they ship, across providers and architectures.\n\n - Proven traction: Build on open-source work with more than 4 million model downloads, and on growing industry partnerships.\n\n - The team: Work with researchers from MIT, Berkeley, and CMU, and with industry veterans from NVIDIA, AMD, Snowflake, and Adobe.\n\n - Compensation: $170,000 to $240,000 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.\n\nLocation: San Francisco, CA. 4 days in office, 1 day remote. \n\nStart date: As soon as available \n\nVisa: We sponsor H-1B and other work visas for exceptional candidates. \n\nLearn more: nunchux.ai http://nunchux.ai \n\nApply: Please apply through our Ashby careers page.\n\nNunchux AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.","compensation":{"compensationTierSummary":"$170K – $240K • Offers Equity","scrapeableCompensationSalarySummary":"$170K - $240K","compensationTiers":[{"id":"5be62536-5707-4364-8ee7-4e39b3788aa6","tierSummary":"Base $170K – $240K • Offers Equity","title":"All","additionalInformation":null,"components":[{"id":"ba304514-c4ed-4c74-8dc4-9e3121d407b4","summary":"Base $170K – $240K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":170000,"maxValue":240000},{"id":"8057dc7e-ebf6-4214-ab17-6652b939cf48","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":170000,"maxValue":240000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}}],"apiVersion":"1"}