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Depending on your role, you will 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.</p>\n<p>We are a frontier research lab and we value obsessions, low ego, boldness, and kindness. 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You&#39;ll build data systems behind the largest training runs on thousands of GPUs, where fixing one bottleneck lets researchers train the next breakthrough model.</p>\n<p><strong>What You&#39;ll Work On:</strong></p>\n<ul>\n<li>Scalable data loaders for training runs across thousands of GPUs</li>\n<li>Efficient storage and retrieval systems for petabyte-scale datasets</li>\n<li>Multi-cloud object storage abstraction</li>\n<li>Execute large-scale data migrations across storage systems and providers</li>\n<li>Debug and resolve performance bottlenecks in distributed data loading</li>\n</ul>\n<p><strong>Technical Focus:</strong></p>\n<ul>\n<li>Python, PyTorch DataLoader internals</li>\n<li>Object storage (e.g. S3, Azure Blob, GCS)</li>\n<li>Parquet for metadata</li>\n<li>Video: ffmpeg, PyAV, codec fundamentals</li>\n</ul>\n<p><strong>What We&#39;re Looking For:</strong></p>\n<ul>\n<li>Built and operated data pipelines at petabyte scale</li>\n<li>Optimized data loading</li>\n<li>Worked with petabyte-scale video and image datasets</li>\n<li>Written processing jobs operating on millions of files</li>\n<li>Debugged distributed system bottlenecks across large fleets of machines</li>\n</ul>\n<p><strong>Nice to Have:</strong></p>\n<ul>\n<li>Experience streaming dataset formats (e.g. WebDataset)</li>\n<li>Video codec internals and frame-accurate seeking</li>\n<li>Distributed systems experience</li>\n<li>Slurm and Kubernetes for job orchestration</li>\n<li>Experience with object storage performance tuning across providers</li>\n</ul>\n<p><strong>How We Work Together:</strong></p>\n<ul>\n<li>We&#39;re a distributed team with real offices that people actually use. 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You will also collaborate with research teams to deeply understand their infrastructure needs and design solutions that balance performance with cost efficiency.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Maintaining research infrastructure, ensuring health, and optimizing components to extract peak performance from the system (both on application and infrastructure side)</li>\n<li>Scaling infrastructure to meet growing research demands while maintaining reliability and performance</li>\n<li>Collaborating with research teams to deeply understand their infrastructure needs, and design solutions that balance performance with cost efficiency</li>\n<li>Identifying and resolving performance bottlenecks and capacity hotspots through deep analysis of distributed systems at scale</li>\n<li>Building and evolving telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets</li>\n<li>Participating in on-call rotations and incident response to maintain system reliability</li>\n</ul>\n<p>Technical focus includes:</p>\n<ul>\n<li>Python, Bash, Go</li>\n<li>Kubernetes</li>\n<li>Nvidia GPU drivers and operators</li>\n<li>OTel, Prometheus</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>Experience building or operating large-scale training platforms</li>\n<li>Worked with large-scale compute clusters (GPUs)</li>\n<li>Proven ability to debug performance and reliability issues across large distributed fleets</li>\n<li>Strong problem-solving skills and ability to work independently</li>\n<li>Strong communication skills and the ability to work effectively with both internal and external partners</li>\n<li>Deep knowledge of modern cloud infrastructure including Kubernetes, Infrastructure as Code, AWS, and GCP</li>\n<li>Experience with SLURM</li>\n</ul>\n<p>We offer a competitive base annual salary of $180,000-$300,000 USD and a hybrid work model with a meaningful in-person presence.</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_c1dcea75-d5a","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/4925659008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000-$300,000 USD","x-skills-required":["Python","Bash","Go","Kubernetes","Nvidia GPU drivers","Nvidia GPU operators","OTel","Prometheus","Experience building or operating large-scale training platforms","Worked with large-scale compute clusters (GPUs)","Proven ability to debug performance and reliability issues across large distributed fleets","Strong problem-solving skills and ability to work independently","Strong communication skills and the ability to work effectively with both internal and external partners","Deep knowledge of modern cloud infrastructure including Kubernetes, Infrastructure as Code, AWS, and GCP","Experience with SLURM"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:25:55.745Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Freiburg (Germany), San Francisco (USA)"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Bash, Go, Kubernetes, Nvidia GPU drivers, Nvidia GPU operators, OTel, Prometheus, Experience building or operating large-scale training platforms, Worked with large-scale compute clusters (GPUs), Proven ability to debug performance and reliability issues across large distributed fleets, Strong problem-solving skills and ability to work independently, Strong communication skills and the ability to work effectively with both internal and external partners, Deep knowledge of modern cloud infrastructure including Kubernetes, Infrastructure as Code, AWS, and GCP, Experience with SLURM","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":300000,"unitText":"YEAR"}}},{"@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. 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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. 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