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As a Senior Account Executive, you will own the end-to-end commercial journey, working closely with research, engineering, and leadership to help customers move confidently from evaluation to production.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own the full sales cycle from discovery through close and expansion across startups, scaleups, and enterprises</li>\n<li>Run complex, multi-stakeholder deals involving technical buyers, executives, and legal teams</li>\n<li>Design and execute sales motions across APIs, on-prem deployments, and custom licensing</li>\n<li>Build durable customer relationships that drive retention and long-term expansion</li>\n<li>Partner closely with solutions engineering, forward-deployed engineers, product, research, and legal on bespoke deals</li>\n<li>Translate customer needs and market signals into actionable feedback for product and research</li>\n<li>Help define early commercial processes, playbooks, and qualification frameworks as we scale</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience selling technical products to tech executives, ML teams, and enterprise stakeholders</li>\n<li>Ability to discuss model capabilities, inference requirements, and integration tradeoffs with technical buyers</li>\n<li>Proven success managing long, multi-stakeholder sales cycles and closing strategic landmark deals</li>\n<li>Strong negotiation skills across pricing, licensing, and custom commercial terms</li>\n<li>Track record building and scaling pipelines across different deal sizes and customer types</li>\n<li>Comfort shaping process and structure in fast-moving, early-stage environments</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Base Annual Salary: EU €150,000 - €185,000 + variable + equity or US $180,000 - $210,000 USD + variable + equity</li>\n<li>Opportunity to work with a cutting-edge research lab</li>\n<li>Collaborative and dynamic work environment</li>\n</ul>\n<p>Values:</p>\n<ul>\n<li>Obsessed: We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.</li>\n<li>Low Ego: The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.</li>\n<li>Bold: We take the ambitious bet. We ship, we do not wait for conditions to be perfect.</li>\n<li>Kind: People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.</li>\n</ul>","enriched_at":1787919177502},{"id":"job_bf902dd9-b6c","title":"Senior Partnerships Manager","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5406421008","location":"San Francisco, CA","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Sales","description":"<p>This role can be office-based in San Francisco, USA or remote-first, with a requirement to be in the office one full week per month.</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.</p>\n<p>This isn&#39;t a &quot;close deals and hit quotas&quot; role. You&#39;ll be architecting the commercial layer of a technology that&#39;s still being invented. What does pricing look like when your product gets exponentially better every quarter? How do you structure partnerships when your API serves both scrappy startups and global enterprises?</p>\n<p>You&#39;ll work directly with our technical team to translate research breakthroughs into market opportunities, often before the market knows it needs them. One week you might be negotiating custom enterprise agreements; the next, designing pricing models for capabilities we just shipped.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Drive business growth through strategic partnerships and custom deal development, from initial outreach through close</li>\n<li>Develop pricing strategies for capabilities that may not have existed six months ago</li>\n<li>Collaborate with Research, Engineering, Finance, and Legal to lead complex product, data, and distribution deals</li>\n<li>Identify and forge new distribution and monetization channels the market hasn&#39;t imagined yet</li>\n<li>Translate technical capabilities into commercial opportunities, working directly with engineers to understand product requirements and limitations</li>\n<li>Structure partnership models that serve both API users and enterprises running models on-premise</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Technical foundation: Either prior experience in image/video generation companies OR a technical background (engineering, solutions architecture, technical product roles)</li>\n<li>Proven track record in BD/partnerships in AI, cloud, or technical infrastructure</li>\n<li>Deep understanding of SaaS business models, API pricing, cloud consulting, and enterprise licensing</li>\n<li>Ability to hold technical conversations with engineers and translate them into commercial strategy</li>\n<li>Strong relationship-building with track record of maintaining long-term partnerships</li>\n<li>Analytical rigor and market research capabilities</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience with diffusion models for image/video generation</li>\n<li>Early-stage, high-growth startup experience</li>\n<li>B2B API and model weight licensing sales models</li>\n<li>Open-source business model experience</li>\n</ul>\n<p>Base Annual Salary: EU €135,000 - €190,000 + variable + equity US $175,000 - $240,000 + variable + equity</p>","enriched_at":1787742383442},{"id":"job_eba4cd44-f27","title":"Field Marketing Manager","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5396777008","location":"San Francisco (USA), Seattle (USA), Los Angeles (USA), Austin (USA), Freiburg (Germany), Berlin (Germany), Munich (Germany), Frankfurt (Germany), Paris (France), Amsterdam (Netherlands), London (United Kingdom)","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Marketing","description":"<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&#39;re just getting started.</p>\n<p>Your job is to turn attention into pipeline. You&#39;ll own two motions where FLUX meets enterprises and partners in person: the events where customers experience the models firsthand, and the co-marketing engine with cloud and distribution partners who bring FLUX to the market. You&#39;ll work directly with GTM, Partnerships, and our creative technologists.</p>\n<h4>Responsibilities</h4>\n<h5>Events</h5>\n<ul>\n<li>Shape BFL&#39;s event calendar end-to-end: tier opportunities, decide where to show up, and run the full events lifecycle.</li>\n<li>Design event experiences that demonstrate the models, working with Creative Technologists and FDEs on interactive demos and installations.</li>\n<li>Build the pre/post event machine: target lists for Sales, meeting orchestration on-site, pipeline attribution afterward.</li>\n<li>Run BFL-hosted formats: customer dinners, launch events, and executive roundtables in key markets.</li>\n</ul>\n<h5>Co-marketing with partners</h5>\n<ul>\n<li>Own the co-marketing motion with distribution and cloud partners; joint announcements, launch-day amplification, case studies.</li>\n<li>Coordinate partner marketing calendars around BFL launches.</li>\n<li>Build repeatable co-marketing playbooks per partner tier.</li>\n</ul>\n<h5>GTM Enablement</h5>\n<ul>\n<li>Pitch decks</li>\n<li>Solution marketing</li>\n</ul>\n<h5>Connecting the motions</h5>\n<ul>\n<li>Co-own the activation calendar with the social marketing team.</li>\n<li>Feed insights from events back into GTM and product marketing.</li>\n</ul>\n<h4>Requirements</h4>\n<ul>\n<li>3–6 years in field/event marketing, partner marketing, or integrated campaigns at a B2B tech company.</li>\n<li>Experience running events end-to-end and showing pipeline results.</li>\n<li>Co-marketing experience with large partners.</li>\n<li>Understanding of enterprise marketing.</li>\n<li>Strong execution skills.</li>\n<li>Visual taste.</li>\n<li>Comfortable with ambiguity and tight timelines.</li>\n</ul>\n<h4>Nice to Have</h4>\n<ul>\n<li>Experience marketing generative AI, creative tooling, or developer-facing infrastructure.</li>\n<li>Relationships in the creative AI community.</li>\n<li>Experience working with cloud or platform partners.</li>\n<li>Built an event or campaign that&#39;s still referenced.</li>\n<li>Time spent working in the US.</li>\n</ul>\n<h4>Compensation</h4>\n<ul>\n<li>Base Annual Salary: US $160,000- $200,000 + equity or EU €105,000- €130,000 + equity</li>\n</ul>","enriched_at":1787386222852},{"id":"job_8178722a-f86","title":"IT Engineer","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5374618008","location":"Freiburg","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"IT","description":"<p>We are seeking an IT Engineer to join our team in Freiburg, Germany. As an IT Engineer, you will take a partially built environment that still runs largely on manual ClickOps and turn it into automated, self-service, code-defined infrastructure for workforce enablement.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own the full endpoint lifecycle for our (currently all-macOS) fleet: purchasing, issuance, zero-touch setup, management, and retrieval.</li>\n<li>Complete our MDM migration across the endpoint fleet.</li>\n<li>Own identity and access across our SaaS vendor stack, and replace manual access administration with an automated joiner-mover-leaver lifecycle (SSO, SCIM, provisioning/de-provisioning).</li>\n<li>Build automation and internal tooling to remove routine human work from the path to staff productivity.</li>\n<li>Define IT infrastructure as code, with configuration, provisioning, and access managed in version control.</li>\n<li>Implement and maintain identity, access, and endpoint security controls for workforce systems, ensuring the fastest path is the secure path.</li>\n<li>Provide workplace technical support in Freiburg and remote support for our San Francisco office.</li>\n<li>Manage video conferencing equipment across offices.</li>\n<li>Develop the tooling and reporting that turn endpoint metrics, tool usage, and support data into a clear picture of our operational health.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Hands-on IT / corporate engineering experience in a fast-growing startup, and comfortable being the primary day-to-day IT contact.</li>\n<li>macOS fleet management, MDM (Iru, Jamf, or similar), Apple Business Manager / ADE, and zero-touch provisioning experience.</li>\n<li>Identity and access administration with an IdP, including SSO/SAML and SCIM-based lifecycle automation across SaaS applications.</li>\n<li>Scripting and automation (e.g. Python, shell) and hands-on infrastructure-as-code experience (e.g. Terraform).</li>\n<li>A security mindset for workforce systems: access, endpoints, and the tradeoffs between control and usability.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience running an MDM migration or standardizing an existing fleet.</li>\n</ul>\n<p>We offer a base annual salary of EU €95,000 - €110,000 + Equity.</p>","enriched_at":1787119376828},{"id":"job_498c4417-8f6","title":"Product Engineer","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5389706008","location":"Freiburg","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"<p>We&#39;re looking for a Product Engineer to build the experiences people meet our models through.</p>\n<p>You&#39;ll work alongside our researchers and infrastructure engineers to turn frontier model capabilities into interfaces that feel fast, considered, and obvious to use.</p>\n<p>You&#39;ll own the surfaces developers and creatives touch first:</p>\n<ul>\n<li>Playground - where most developers and creatives experience and test our models</li>\n<li>Dashboard - where developers manage keys, usage, and billing</li>\n<li>Launch demos for new FLUX models, often built against checkpoints nobody outside the lab has seen</li>\n<li>Reference implementations and starter apps that other developers build on top of</li>\n</ul>\n<p>Day to day, that means:</p>\n<ul>\n<li>Shipping high-quality web applications end to end, fast</li>\n<li>Turning research capabilities into product - first prototype through to production</li>\n<li>Owning frontend architecture and the practices the rest of the engineering team builds on</li>\n<li>Setting the visual bar - the shared components, interaction detail, and polish that everything else gets held against</li>\n<li>Designing for genuinely hard interaction problems: long-running generations, iterative editing, large media payloads, real-time inference</li>\n<li>Driving performance for high-volume media processing and latency that users actually feel</li>\n<li>Working with research, infra, growth, and design to decide what&#39;s worth building at all</li>\n</ul>\n<p>Most of our code is written by agents. The job is deciding what to build, decomposing it so agents can execute well, and reviewing what comes back with enough judgment to catch what&#39;s subtly wrong.</p>\n<p>We care far more about how quickly you can go from idea to working, reviewed, shipped product than about which frameworks you&#39;ve memorized.</p>\n<p>This is a frontend-leaning full-stack role. You&#39;ll live in the frontend and own it, but you&#39;ll be comfortable following a feature into the API layer, the database, and a migration without waiting for someone else.</p>\n<h3>What We&#39;re Looking For</h3>\n<ul>\n<li>A track record of shipping real products that real users depend on, and the war stories to go with it</li>\n<li>Fluency driving coding agents on non-trivial existing codebases, not just greenfield demos , plus the judgment to review generated code critically, catching the plausible-but-wrong, the insecure, and the unmaintainable</li>\n<li>A clear view on where agents should not be trusted, and why</li>\n<li>Visual craft , your interfaces look considered, and you can tell the difference between shipped-and-fine and actually good. Comfortable working from a Figma file or from nothing</li>\n<li>Strong product sense: you make UX and technical tradeoffs deliberately, and can explain them to people who don&#39;t write code</li>\n<li>Excellent debugging instincts, wherever the bug turns out to live</li>\n<li>Comfort with the ambiguity and pace of an early-stage lab , loose requirements, shifting priorities, and nobody to tell you what to do next</li>\n</ul>\n<h3>Nice to have:</h3>\n<ul>\n<li>Experience with React, TypeScript, and Next.js (we use them - we just don&#39;t think they&#39;re the hard part)</li>\n<li>A track record of building developer tools or creative applications</li>\n<li>Previous experience building interfaces on top of generative models, and working knowledge of AI/ML concepts</li>\n<li>Instrumentation and experimentation experience: analytics, A/B testing, activation funnels</li>\n<li>Contributions to open-source projects</li>\n</ul>\n<h3>What we offer you:</h3>\n<ul>\n<li>The chance to put the first interface on frontier image and video models, sometimes before anyone outside the lab has seen them</li>\n<li>Work alongside the researchers who invented much of modern generative media</li>\n<li>Competitive salary, equity and benefits programs</li>\n<li>Hybrid working environment from our HQ in Freiburg (Germany) with regular global off-sites</li>\n</ul>","enriched_at":1787033590212},{"id":"job_6a836184-c87","title":"Member of Technical Staff - Research Engineer","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5286037008","location":"San Francisco, USA","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"<p>We are seeking a Member of Technical Staff - Research Engineer to join our team in San Francisco. You will be embedded in production training and help solve the hardest systems and performance problems. Your output will often be code, measurements, kernels, debugging tools, and training-system changes that make better research possible.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Improve the performance, reliability, and numerical stability of production training runs for large multimodal generative models</li>\n<li>Profile full training steps across model code, attention, kernels, data loading, encoders, communication, optimizer steps, checkpointing, and memory pressure</li>\n<li>Implement and validate GPU-level optimizations: fused kernels, attention paths, low-precision matmuls, quantization kernels, CUDA/Triton/CuTe/CUTLASS experiments, and no-compile alternatives where they make sense</li>\n<li>Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk, and quality tradeoffs against baseline training runs</li>\n<li>Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from changes that only look good in a microbenchmark</li>\n<li>Debug distributed training failures: NaNs, loss spikes, silent numerical drift, memory leaks, stragglers, bad nodes, NCCL issues, and throughput cliffs</li>\n<li>Build benchmarking and profiling harnesses that make performance claims trustworthy across hardware, shapes, sequence lengths, and training configurations</li>\n<li>Help the training team move quickly when an urgent bottleneck appears, while turning repeated failures into better abstractions and tools</li>\n</ul>\n<p><strong>Requirements:</strong></p>\n<ul>\n<li>Experience working deeply on large-scale training systems, ideally as part of a training group working closely with researchers</li>\n<li>Strong PyTorch fluency, including comfort reading and modifying low-level training code rather than only using high-level APIs</li>\n<li>Experience with distributed training concepts such as FSDP, tensor/model/context/sequence parallelism, activation checkpointing, NCCL, and overlapping compute and communication</li>\n<li>Hands-on experience improving training throughput, memory footprint, or stability in real training runs</li>\n<li>Experience profiling GPU workloads with tools like Nsight Systems, Nsight Compute, torch profiler, trace viewers, or custom telemetry</li>\n<li>Practical GPU performance judgment: you may use modern coding agents and tools as much as you want, but you need the understanding to verify correctness, numerical behavior, and performance, and to own the result</li>\n<li>Understanding of low-precision training and quantization tradeoffs: FP8, MXFP8, FP4/NVFP4-style formats, scaling, accumulation, numerical validation, and convergence risk</li>\n<li>Good research judgment: you can partner with researchers on ablations, understand what the measurements do and do not prove, and keep optimization work tied to model-quality outcomes</li>\n<li>Comfortable operating in ambiguity: sometimes the task is a clean implementation, sometimes it is a production fire, and sometimes it is figuring out which of three plausible explanations is actually true</li>\n</ul>\n<p><strong>Benefits:</strong></p>\n<ul>\n<li>Base Annual Salary: US $180,000 - $290,000 + equity</li>\n<li>Flexible work arrangements: 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</li>\n<li>Reasonable travel costs covered</li>\n</ul>\n<p><strong>Values:</strong></p>\n<ul>\n<li>Obsessed: We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.</li>\n<li>Low Ego: The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.</li>\n<li>Bold: We take the ambitious bet. We ship, we do not wait for conditions to be perfect.</li>\n<li>Kind: People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.</li>\n</ul>","enriched_at":1782789468187},{"id":"job_f9fc17d6-a60","title":"Developer Experience Engineer","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008","location":"Freiburg (Germany)","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"<p>We&#39;re hiring a Developer Experience Engineer to own the developer experience end-to-end, making Black Forest Labs the platform that wins on developer experience. You&#39;ll work with the API/platform engineering team, research, and DevRel to design and ship features that improve the developer experience. You&#39;ll partner with platform engineering on API design, work with product engineering on developer-facing surfaces, and own the portfolio of entry points developers and agents reach for.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own DevExp as a product surface - wherever the bottleneck is, that&#39;s where you ship next.</li>\n<li>Partner with platform engineering on API design before it ships, not after - bring the developer-and-agent perspective into the room when the contract is being decided.</li>\n<li>Work with product engineering on the developer-facing surfaces of the dashboard - playground, API keys, usage, request inspectors, webhook debugging - bringing the DevExp lens to features the rest of the team is already shipping.</li>\n<li>Make Black Forest Labs the best visual AI platform for agents to use. Own the MCP server, skills, agent onboarding paths, and the machine-readable surfaces of the platform.</li>\n<li>Design the reference patterns and SDK primitives developers reach for when building agentic workflows on FLUX.</li>\n<li>Stress-test every API and SDK change against &#39;would Claude / GPT / an autonomous agent get this right on the first try?&#39; - and treat agents as a first-class user across the whole product.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>4+ years building software, with meaningful time on developer-facing products - SDKs, APIs, dashboards, consoles, platforms developers actually use.</li>\n<li>Full-stack comfortable. TypeScript / React / Next on the front, Python (and ideally Go or Rust) on the back.</li>\n<li>Deep instincts on API design - you have opinions about error formats, pagination, async job shapes, breaking changes, and you&#39;ve lived with the consequences of bad ones.</li>\n<li>Fluent in the 2026 agent stack - MCP, tool use, the major agent runtimes - and you know what breaks when an LLL is the one reading your error message or browsing your API.</li>\n<li>You&#39;ve shipped developer-facing product that real external developers depend on. Not internal tooling.</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>Background at a developer platform where DevExp was a top-level metric (Stripe, Vercel, Cloudflare, Modal, Replicate, Resend, Linear, Anthropic platform, etc.).</li>\n<li>You&#39;ve built or meaningfully contributed to an MCP server real users / agents rely on.</li>\n<li>Experience with media-generation or other high-latency, async, job-shaped APIs - and the UX puzzles that come with them (polling, webhooks, partial results, cost surprises).</li>\n<li>A public surface - OSS, a writeup, a dashboard you&#39;re proud of - that lets us see your taste before the interview.</li>\n</ul>","enriched_at":1779959138235},{"id":"job_23f7c411-b12","title":"Member of Technical Staff - Model Serving / API Backend Engineer","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/4132239008","location":"Freiburg (Germany), San Francisco (USA)","job_type":"full-time","experience_level":"staff","work_arrangement":"hybrid","category":"Engineering","description":"<p>About Black Forest Labs</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.</p>\n<p>Our research team moves fast. Models improve weekly. New capabilities emerge constantly. What slows us down is not model quality,it’s productionization. Without this role:</p>\n<ul>\n<li>Research checkpoints sit longer before becoming usable APIs</li>\n<li>Inference is slower than it needs to be</li>\n<li>APIs struggle under load</li>\n<li>Demos don’t reflect the true potential of our models</li>\n</ul>\n<p>This role removes the bottleneck between frontier research and production reality. Once hired, researchers ship faster, demos launch faster, and customers experience models at their best.</p>\n<p>What You’ll Work On</p>\n<p>You will own the bridge between research breakthroughs and production systems.</p>\n<ul>\n<li>Turn research checkpoints into production-ready inference services</li>\n<li>Design and maintain high-performance APIs serving millions of requests</li>\n<li>Optimize inference latency and throughput across GPU infrastructure</li>\n<li>Build scalable serving architectures that handle unpredictable traffic</li>\n<li>Improve reliability, monitoring, and observability across model-serving systems</li>\n<li>Prototype and ship demos that showcase new capabilities in days, not weeks</li>\n<li>Collaborate closely with researchers to move from idea to live endpoint rapidly</li>\n</ul>\n<h3>Tools &amp; Context</h3>\n<p>– Model Serving &amp; API Infrastructure</p>\n<ul>\n<li>Python, FastAPI, async systems</li>\n<li>GPU infrastructure, CUDA, inference optimization</li>\n<li>Docker and Kubernetes</li>\n<li>Redis, Postgres, distributed task queues</li>\n<li>Cloud platforms (AWS, GCP, or Azure)</li>\n<li>Observability stacks (metrics, logging, tracing)</li>\n</ul>\n<p>This role spans backend systems, GPU performance, and production ML serving.</p>\n<h3>What We’re Looking For</h3>\n<p>You’ve built and operated systems at meaningful scale. You understand the difference between a research prototype and a production system. You are comfortable navigating ambiguity, making tradeoffs, and improving systems under real-world constraints. You demonstrate:</p>\n<ul>\n<li>Strong judgment around performance, reliability, and cost tradeoffs</li>\n<li>Experience scaling APIs or ML systems under load</li>\n<li>Comfort working in fast-moving, research-adjacent environments</li>\n<li>Ownership from system design through debugging and deployment</li>\n</ul>\n<p>Role-specific experience we value:</p>\n<ul>\n<li>Building and operating ML inference services in production</li>\n<li>Designing scalable API architectures with async processing</li>\n<li>Optimizing GPU workloads (batching, quantization, compilation, CUDA)</li>\n<li>Managing distributed systems and task queues under variable load</li>\n<li>Implementing monitoring and observability for production ML systems</li>\n<li>Debugging performance bottlenecks across model, infrastructure, and network layers</li>\n</ul>\n<p>Bonus experience includes:</p>\n<ul>\n<li>Real-time or low-latency inference systems</li>\n<li>TensorRT, reduced precision, layer fusion, or model compilation techniques</li>\n<li>Frontend demo tooling (Streamlit, Gradio, React)</li>\n<li>CI/CD and automated testing for ML systems</li>\n<li>Security best practices for API and model serving</li>\n</ul>\n<h3>How We Work Together</h3>\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.</p>\n<p>Everything we do is grounded in four values:</p>\n<ul>\n<li>Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.</li>\n<li>Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.</li>\n<li>Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.</li>\n<li>Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.</li>\n</ul>\n<p>Base Annual Salary: $180,000–$300,000 USD</p>","enriched_at":1778623546730},{"id":"job_9c1b936e-47a","title":"Member of Technical Staff - Post Training","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5193512008","location":"Freiburg (Germany)","job_type":"full-time","experience_level":null,"work_arrangement":"hybrid","category":"Engineering","description":"<h4>About the Role</h4>\n<p>We&#39;re seeking a Member of Technical Staff to own the post-training pipeline for our multimodal models end to end. This includes data strategy and reward modeling, preference optimization, distillation, and safety tuning across image, editing, and video.</p>\n<h4>Responsibilities</h4>\n<ul>\n<li>Own the full post-training pipeline end to end , from data curation and reward modeling through fine-tuning, preference optimization, distillation, safety tuning, evaluation, and deployment</li>\n<li>Advance techniques across the post-training stack: SFT, RLHF, RLAIF, DPO, preference learning, and reward modeling to align models with human intent and aesthetic judgment</li>\n<li>Work across modalities: text-to-image, image editing, multi-reference, and video post-training</li>\n<li>Build personalization and customization capabilities that let users adapt our models to their own creative style</li>\n<li>Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team</li>\n<li>Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering</li>\n</ul>\n<h4>Requirements</h4>\n<ul>\n<li>You&#39;ve owned post-training for a frontier generative model through release (SFT, preference optimization (DPO or RLHF), distillation, safety tuning) with measurable quality wins on human prefs or standard benchmarks</li>\n<li>Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization</li>\n<li>Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video</li>\n<li>Strong PyTorch fluency; you write research code that others can build on</li>\n<li>Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus</li>\n<li>Bias toward shipping: measurable model-quality improvements that reach users, not just papers</li>\n</ul>\n<h4>How We Work Together</h4>\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>","enriched_at":1777043956089},{"id":"job_556f5f38-c43","title":"Member of Technical Staff - VLM","source_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008","location":"Freiburg (Germany)","job_type":"full-time","experience_level":null,"work_arrangement":"hybrid","category":"Engineering","description":"<h4>About This Role</h4>\n<p>We&#39;re seeking a Member of Technical Staff to pioneer the integration of vision-language models (VLMs) into our FLUX stack. As a key member of our team, you&#39;ll develop novel approaches, innovate on architectures, and answer questions that haven&#39;t been solved yet.</p>\n<h4>What You&#39;ll Work On</h4>\n<ul>\n<li>Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack , innovating on architectures, not just applying existing ones</li>\n<li>Design fine-tuning strategies that adapt VLMs to specialized creative use cases (captioning, editing instructions, prompt enhancement) that general-purpose models can&#39;t handle</li>\n<li>Research integrations between VLM/LLM capabilities and our diffusion and flow pipelines , finding creative ways to improve generation quality and controllability without computational bottlenecks</li>\n<li>Evaluate emerging multimodal architectures, translating the best of recent research into practical improvements</li>\n</ul>\n<h4>What We&#39;re Looking For</h4>\n<ul>\n<li>You&#39;ve pretrained or significantly advanced a VLM (not just SFT&#39;d or LoRA&#39;d one) that was deployed in a production system or released publicly</li>\n<li>Strong publication record or unambiguous production track record showing you push the frontier on multimodal architectures</li>\n<li>Deep understanding of how vision and language representations interact: tokenization, alignment, grounding, cross-modal attention, and the failure modes of each</li>\n<li>Experience with distributed training at multi-node scale</li>\n<li>Comfortable at the research/production boundary , you care whether the work ships and generalizes, not just whether it reads well</li>\n<li>Experience with diffusion or flow-based generative models is a strong plus , especially if you&#39;ve thought about how autoregressive and diffusion paradigms can compose</li>\n</ul>\n<h4>How We Work Together</h4>\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.</p>","enriched_at":1777043878225}],"category_normalised":[{"category":"engineering","count":9},{"category":"sales","count":3},{"category":"marketing","count":1},{"category":"it","count":1}],"velocity":{"weeks":[{"week_start":"2026-04-13","count":3},{"week_start":"2026-04-20","count":3},{"week_start":"2026-05-11","count":1},{"week_start":"2026-05-25","count":1},{"week_start":"2026-06-29","count":1},{"week_start":"2026-08-17","count":3},{"week_start":"2026-08-24","count":2}],"trend":"stable","wow_pct":-33},"momentum":{"recent_14d":0,"prior_14d":2,"growth_pct":-100,"classification":"stable"},"salary_vs_industry":{"company_median":180000,"industry_median":null,"percentile":null,"sample_size":5,"by_region":[{"region":"United States","company_median":235000,"industry_median":null,"sample":3},{"region":"Unknown","company_median":145787.5,"industry_median":null,"sample":2}],"transparency_pct":36,"industry_transparency_pct":0,"transparency_warning":false},"market_share":{"company_jobs":14,"industry_total":14,"share_pct":100,"rank":1,"peer_count":1},"ai_exposure":{"occupation_weighted_score":0.264,"skill_weighted_score":0.331,"top_exposed_titles":[],"top_exposed_skills":[{"skill":"PyTorch","count":4,"score":0.331}]},"peer_set":[],"skills_lq":[],"geographic_shift":{"current":[{"region":"EU","count":6,"share_pct":42.9},{"region":"United States","count":5,"share_pct":35.7},{"region":"Unknown","count":3,"share_pct":21.4}],"emerging":[],"shrinking":[]},"seniority_anomalies":{"exec_recent_30d":0,"exec_prior_90d_avg":0,"exec_growth_pct":0,"notable_exec_hires":[]},"posting_dynamics":{"median_days_open":66,"industry_median_days_open":null,"long_open_count":8,"closure_rate_pct":14}}}}