{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/title/member-of-technical-staff-image-video-generation"},"x-facet":{"type":"title","slug":"member-of-technical-staff-image-video-generation","display":"Member of Technical Staff - Image / Video Generation","count":1},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5c28c97d-fc5"},"title":"Member of Technical Staff - Image / Video Generation","description":"<p><strong>Job Title</strong></p>\n<p>Member of Technical Staff - Image / Video Generation</p>\n<p><strong>Job Description</strong></p>\n<p>We&#39;re the team behind Latent Diffusion, Stable Diffusion, and FLUX,foundational technologies that changed how the world creates images and video. We&#39;re creating the generative models that power how people make images and video,tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.</p>\n<p><strong>Why This Role</strong></p>\n<p>You&#39;ll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn&#39;t about following established recipes,it&#39;s about running the experiments that clarify which architectural choices matter and which are less impactful.</p>\n<p><strong>What You’ll Work On</strong></p>\n<ul>\n<li>Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters</li>\n<li>Rigorously ablates design choices,running experiments that isolate variables, control for confounds, and produce insights you can actually trust,then communicating those results to shape our research direction</li>\n<li>Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously</li>\n<li>Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren&#39;t enough</li>\n</ul>\n<p><strong>What We’re Looking For</strong></p>\n<ul>\n<li>You&#39;ve trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You&#39;re comfortable debugging distributed training issues and presenting research findings to the team.</li>\n</ul>\n<p><strong>Required Skills</strong></p>\n<ul>\n<li>Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training</li>\n<li>Experience fine-tuning diffusion models for specialized applications,upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture</li>\n<li>Deep understanding of how to effectively evaluate image and video generative models,knowing which metrics correlate with quality and which are just convenient proxies</li>\n<li>Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning</li>\n<li>Solid understanding of distributed training techniques,FSDP, low precision training, model parallelism,because our models don&#39;t fit on one GPU and training decisions impact research outcomes</li>\n</ul>\n<p><strong>Preferred Skills</strong></p>\n<ul>\n<li>Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors</li>\n<li>Proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers</li>\n<li>Know the performance characteristics of different architectural choices at scale</li>\n<li>Have published research that contributed to how people think about generative models</li>\n</ul>\n<p><strong>How We Work Together</strong></p>\n<p>We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_5c28c97d-fc5","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Black Forest Labs","sameAs":"https://www.blackforestlabs.com/","logo":"https://logos.yubhub.co/blackforestlabs.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["large-scale diffusion models","image and video data","PyTorch","transformer architectures","distributed training techniques"],"x-skills-preferred":["writing forward and backward Triton kernels","profiling, debugging, and optimizing single and multi-GPU operations","published research on generative models"],"datePosted":"2026-04-17T12:25:33.116Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Freiburg (Germany)"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"large-scale diffusion models, image and video data, PyTorch, transformer architectures, distributed training techniques, writing forward and backward Triton kernels, profiling, debugging, and optimizing single and multi-GPU operations, published research on generative models"}]}