About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

You'll architect the financial planning backbone for a company scaling faster than traditional frameworks can handle. This isn't about maintaining existing budgets—it's about building planning processes, financial models, and decision frameworks that help leadership allocate resources in a market that's evolving in real time.

What You’ll Work On

You'll be the person who:

• Owns and drives the full planning cycle—annual operating plans, quarterly forecasts, rolling projections, and long-range strategic planning tailored to AI research timelines (spoiler: annual budgets don't work)
• Builds sophisticated financial models covering multiple revenue streams (API, Enterprise, Playground), GPU compute economics, headcount planning, and cash flow across US and German entities
• Delivers monthly and quarterly business reviews with variance analysis, KPIs, and actionable insights that support executive planning and board reporting
• Leads scenario planning for decisions most companies don't face: GPU provider contracts, go-to-market expansion, pricing frameworks, and R&D investment allocation where one breakthrough hire might 10x capabilities
• Partners with GTM leadership on revenue forecasting when your "pipeline" includes researchers, startups, and Fortune 500s with completely different economics
• Collaborates with Engineering on GPU compute optimization and infrastructure planning where the input is "we need more GPUs" and your job is figuring out how many, when, and from which providers
• Builds executive dashboards tracking what actually matters: ARR growth, customer cohort economics, gross margins, burn rate, and the relationship between model quality and infrastructure cost
• Designs scalable FP&A processes and drives automation across financial planning—working with Accounting to ensure data integrity and reporting consistency
• Supports high-impact strategic initiatives: pricing optimization, enterprise contract structuring, customer segmentation economics, and fundraising support

What We’re Looking For

You've built FP&A in high-growth environments where "best practices" are still being written. You're equally comfortable building three-statement models from scratch as you are explaining complex tradeoffs to non-finance stakeholders. You understand that perfect forecasts are impossible, but disciplined planning is essential. You move fast, think in systems, and know when precision matters versus when directional clarity is enough.

You likely have:

• 6-10 years in FP&A, corporate finance, investment banking, or strategic finance, with at least 4 years hands-on FP&A experience at a high-growth company
• Proven track record owning full planning cycles (annual budgeting, quarterly forecasts, long-range planning) at a B2B SaaS, AI, or technology company
• Advanced Excel/Google Sheets modeling skills—you build complex financial models from first principles, not templates
• Fluency in SaaS metrics (ARR/MRR, NDR, CAC, LTV, payback period, gross margin, Rule of 40) and ideally consumption-based pricing models
• Experience with modern finance tech stack and genuine curiosity about AI-powered finance workflows versus legacy systems
• Comfort with international operations, multi-entity financial structures, and US GAAP
• Ability to work with large datasets, perform deep variance analysis, and build dashboards that executives actually use

We'd be especially excited if you:

• Have experience with usage-based or consumption revenue models, API pricing structures, or GPU/cloud infrastructure economics
• Understand subscription economics alongside usage-based pricing in technical or developer-focused markets
• Bring exposure to enterprise contract structuring and technical sales processes
• Have worked somewhere that scaled 3-5x and had to rebuild planning processes mid-flight
• Are intellectually honest about uncertainty—you can say "here's what we don't know yet" without flinching

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

We're not just tracking expenses—we're building the financial architecture that enables a frontier AI company to make disciplined decisions at breakthrough speed. Every model you build gives leadership better visibility. Every process you implement accelerates decision-making. Every scenario you analyze shapes how we allocate resources between growth and efficiency, research investment and commercialization. If that sounds more compelling than optimizing existing budgets, we should talk.

Base Annual Salary: $165,000–$210,000 USD

We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

This isn't a "close deals and hit quotas" role. You'll be architecting the commercial layer of a technology that'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? What distribution channels don't exist yet—but should?

You'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.

What You’ll Work On

• Drive business growth through strategic partnerships and custom deal development, from initial outreach through close
• Develop pricing strategies for capabilities that may not have existed six months ago
• Collaborate with Research, Engineering, Finance, and Legal to lead complex product, data, and distribution deals
• Identify and forge new distribution and monetization channels the market hasn't imagined yet
• Translate technical capabilities into commercial opportunities, working directly with engineers to understand product requirements and limitations
• Structure partnership models that serve both API users and enterprises running models on-premise

What We’re Looking For

You have deep technical fluency. Either you've worked in image/video generation (at companies building diffusion models, creative tools, or visual AI products) or you come from a technical background—solutions architect, engineer, technical product manager. You can discuss model architectures, inference optimization, and training pipelines with our research team, then translate those capabilities into commercial value propositions.

You've done serious business development in technical domains where you had to learn fast and think structurally. You understand SaaS business models, API pricing, and enterprise licensing. You're comfortable with ambiguity—when a partner asks "Can your model do X?" and X is something we could build but haven't yet, you know how to navigate that conversation honestly.

You care about craft. You want to work with a small, technically exceptional team that's building in public and pushing boundaries. You have strong opinions about how this market should evolve, and you're ready to shape it.

Required:

• Technical foundation: Either prior experience in image/video generation companies OR a technical background (engineering, solutions architecture, technical product roles)
• Proven track record in BD/partnerships in AI, cloud, or technical infrastructure
• Deep understanding of SaaS business models, API pricing, cloud consulting, and enterprise licensing
• Ability to hold technical conversations with engineers and translate them into commercial strategy
• Strong relationship-building with track record of maintaining long-term partnerships
• Analytical rigor and market research capabilities

Nice to have:

• Experience with diffusion models for image/video generation
• Early-stage, high-growth startup experience
• B2B API and model weight licensing sales models
• Open-source business model experience

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary (SF based role): $160,000 - $225,000 USD

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

Our models are seeing strong demand across startups, AI-native companies, and some of the world's most recognized brands. We need someone who can turn that demand into revenue — fast, consistently, and across a high volume of accounts.

This is a full-cycle role: you own everything from first touch to close to renewal and upsell. Some of your accounts will be fast-moving AI startups. Others will be household-name enterprises starting with a focused use case — and it's your job to land them, prove value, and grow them into strategic relationships.

If you're already closing deals and want to be at the forefront of the generative AI wave, this is the role.

What You’ll Work On

• Own the full sales cycle from qualification through close for SMB, startup, AI-native, and enterprise customers — including well-known global brands entering generative AI
• Manage a book of 30-50+ accounts, driving retention, adoption, and expansion — turn initial use cases into broader adoption across teams, products, and business units
• Qualify inbound leads and run outbound prospecting into target segments
• Execute sales campaigns in collaboration with sales leadership
• Handle deals across API access and weight licensing
• Translate customer needs and usage patterns into expansion opportunities
• Collaborate with solutions engineering and product on technical questions

What We’re Looking For

You've closed real deals, you move fast, and you're hungry to have outsized impact at a company where your work directly drives revenue.

• 2-4 years in a closing sales role, ideally with a BDR-to-AE progression
• Experience selling technical products — APIs, developer tools, cloud infrastructure, or AI/ML products
• Comfortable discussing model capabilities, API integration, and deployment with technical buyers
• Track record of managing a high volume of accounts and deals in parallel without dropping balls
• Strong outbound instincts — can build pipeline, not just work inbound
• Experience in a startup, scale-up, or high-growth environment where process didn't exist yet
• Organized, disciplined, and self-directed
• Bonus: background in generative AI, image/video generation, or open-source business models

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary: $140,000 - $170,000 USD + bonus + equity

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

You'll design, deploy, and maintain the ML infrastructure backbone that makes frontier AI research possible. This isn't abstract systems work—every decision you make directly impacts whether a multi-week training run succeeds, whether inference stays fast enough for production, whether researchers can iterate quickly or wait hours for resources.

What You’ll Work On 

You'll be the person who:

• Designs, deploys, and maintains cloud-based ML training clusters (Slurm) and inference clusters (Kubernetes) that researchers and products depend on
• Implements and manages network-based cloud file systems and blob/S3 storage solutions optimized for ML workloads at scale
• Develops and maintains Infrastructure as Code (IaC) for resource provisioning—because manual configuration doesn't scale and configuration drift breaks things
• Implements and optimizes CI/CD pipelines for ML workflows, making it easy for researchers to go from experiment to production
• Designs and implements custom autoscaling solutions for ML workloads where standard approaches fall short
• Ensures security best practices across the ML infrastructure stack without creating friction that slows down research
• Provides developer-friendly tools and practices that make ML operations efficient—because infrastructure that's hard to use doesn't get used

What We’re Looking For

You've built and managed ML infrastructure at scale and understand that supporting AI research is fundamentally different from traditional cloud infrastructure. You've been paged because a training run failed. You've debugged why storage became the bottleneck. You know the difference between infrastructure that works in demos and infrastructure that works when researchers depend on it for months-long experiments.

You likely have:

• Strong proficiency in cloud platforms (AWS, Azure, or GCP) with focus on ML/AI services—you know which services matter and which are marketing
• Extensive experience with Kubernetes and Slurm cluster management in production environments
• Expertise in Infrastructure as Code tools (Terraform, Ansible, etc.) and the discipline to actually use them
• Proven track record managing and optimizing network-based cloud file systems and object storage for ML workloads
• Experience with CI/CD tools and practices (CircleCI, GitHub Actions, ArgoCD, etc.) in ML contexts
• Strong understanding of security principles and best practices in cloud environments—without making security the enemy of velocity
• Experience with monitoring and observability tools (Prometheus, Grafana, Loki, etc.) that help you understand what's actually happening
• Familiarity with ML workflows and GPU infrastructure management—you understand what researchers need
• Demonstrated ability to handle complex migrations and breaking changes in production environments without losing data or breaking experiments

We'd be especially excited if you:

• Have experience building custom autoscaling solutions for ML workloads that standard tools can't handle
• Bring knowledge of cost optimization strategies for cloud-based ML infrastructure (because GPU hours add up)
• Are familiar with MLOps practices and tools
• Have experience with high-performance computing (HPC) environments
• Understand data versioning and experiment tracking for ML
• Know network optimization techniques for distributed ML training
• Have worked with multi-cloud or hybrid cloud architectures
• Are familiar with container security and vulnerability scanning tools

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary: $180,000–$300,000 USD

We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

We're looking for engineers to build and maintain the engine that powers our mission to develop visual intelligence. From maintaining and scaling clusters, to building research platforms to accelerate the rate of innovation, this team operates with large breadth and depth. We build the systems to make multi-week/month long training possible, to orchestrate resources at scale, and at the same time efficiently, enabling the next breakthrough model. If you’re obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement, this team would be perfect for you.

What You’ll Work On

• Maintain research infrastructure, ensuring health, and optimizing components to extract peak performance from the system (both on application, and infrastructure side)
• Scale infrastructure to meet growing research demands while maintaining reliability and performance
• Collaborate with research teams to deeply understand their infrastructure needs, and design solutions that balance performance with cost efficiency.
• Identify and resolve performance bottlenecks and capacity hotspots through deep analysis of distributed systems at scale.
• Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets.
• Participate in on-call rotations and incident response to maintain system reliability

Technical Focus

• Python, Bash, Go
• Kubernetes
• Nvidia GPU drivers, and operators
• OTel, Prometheus

What We’re Looking For

• 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
• Experience building or operating large-scale training platforms

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary: $180,000–$300,000 USD

This role is based in our Freiburg / San Francisco office. We operate a hybrid model and cover reasonable travel costs — relocation is encouraged but not required. We do expect a meaningful in-person presence, and we'll discuss what that looks like for your situation during the process.

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

Vision-language models are becoming foundational to how people interact with generative AI — but most VLM research happens in isolation from the generation stack. At Black Forest Labs, we're integrating VLMs directly into FLUX in ways that make our models more powerful, more controllable, and more aligned with what creators actually want.

This role is about pioneering that integration. You won't be applying off-the-shelf VLMs — you'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet: how vision and language representations inform each other, how multimodal understanding improves generation quality, and how to make these capabilities deployable at scale without compromising what makes FLUX exceptional.

This is a Staff / Senior IC role. We're looking for someone who has pretrained or significantly advanced a VLM, not just fine-tuned one.

What You'll Work On

• 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
• Design fine-tuning strategies that adapt VLMs to specialized creative use cases (captioning, editing instructions, prompt enhancement) that general-purpose models can't handle
• Research integrations between VLM/LLM capabilities and our diffusion and flow pipelines — finding creative ways to improve generation quality and controllability without computational bottlenecks
• Evaluate emerging multimodal architectures, translating the best of recent research into practical improvements

What We're Looking For

• You've pretrained or significantly advanced a VLM (not just SFT'd or LoRA'd one) that was deployed in a production system or released publicly
• Strong publication record or unambiguous production track record showing you push the frontier on multimodal architectures
• Deep understanding of how vision and language representations interact: tokenization, alignment, grounding, cross-modal attention, and the failure modes of each
• Experience with distributed training at multi-node scale
• Comfortable at the research/production boundary — you care whether the work ships and generalizes, not just whether it reads well
• Experience with diffusion or flow-based generative models is a strong plus — especially if you've thought about how autoregressive and diffusion paradigms can compose

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

Post-training is where a foundation model becomes a product. In this role, you'll own the post-training pipeline for our multimodal models end to end — from data strategy and reward modeling to preference optimization, distillation, and safety tuning — across image, editing, and video. You'll drive measurable gains in model quality, build the infrastructure that lets the whole research team iterate fast, and push the state of the art in what it means to align a generative model to human intent.

This is a Staff / Senior IC role. We're looking for someone who has shipped post-training for a frontier model before and wants to do it again.

What You'll Work On

• 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
• 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
• Work across modalities: text-to-image, image editing, multi-reference, and video post-training
• Build personalization and customization capabilities that let users adapt our models to their own creative style
• Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team
• Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering

What We're Looking For

• You'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
• Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization
• Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video
• Strong PyTorch fluency; you write research code that others can build on
• Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus
• Bias toward shipping: measurable model-quality improvements that reach users, not just papers

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

We're building the foundation models that power the next wave of visual intelligence — and pretraining is where that work begins. This role sits at the center of our research effort. You'll shape training objectives, architectures, data strategies, and systems behind our joint image, video, and audio foundation models, with a direct line from your research to products used by millions.

This is a Staff / Senior IC role. We're looking for someone who has already led pretraining at the frontier and wants to do it again.

What You'll Work On

• Lead large-scale pretraining experiments for our multimodal (image, video, audio) foundation models (architecture, objective functions, scaling strategies)
• Develop and evaluate novel ideas across architecture, optimizers, and training algorithms.
• Contribute across the full stack: low-level GPU and systems optimizations, research code, and high-level model design
• Lead focused research projects independently and drive larger cross-team initiatives

What We're Looking For

• You've led or co-owned pretraining for a foundation model (image, video, LLM, or multimodal) that shipped to production or a major release
• Own architectural calls that move the model: attention patterns, modulation schemes, loss formulations, tokenization strategies
• Deep experience with large-scale distributed training: FSDP/TP/PP, multi-node runs at 500+ GPUs, debugging loss spikes, NaNs, throughput regressions, and silent correctness issues at scale
• Strong intuition for architecture and objective design — you've made calls on attention patterns, modulation schemes, or loss formulations that moved a real model
• Track record of shipping: top-venue publications (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) paired with production impact, or unambiguous production wins at a frontier lab
• Deep Python and PyTorch proficiency; comfortable reading and modifying low-level training code
• Familiarity with visual generative models is a must

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

We're looking for infrastructure engineers who want to work at peta-to-exabyte scale. You'll build the data systems behind the largest training runs on thousands of GPUs, where fixing one bottleneck lets researchers train the next breakthrough model.

What You’ll Work On

• Scalable data loaders for training runs across thousands of GPUs
• Efficient storage and retrieval systems for petabyte-scale datasets
• Multi-cloud object storage abstraction
• Execute large-scale data migrations across storage systems and providers
• Debug and resolve performance bottlenecks in distributed data loading

Technical Focus

• Python, PyTorch DataLoader internals
• Object storage (e.g. S3, Azure Blob, GCS)
• Parquet for metadata
• Video: ffmpeg, PyAV, codec fundamentals

What We’re Looking For

• Built and operated data pipelines at petabyte scale
• Optimized data loading
• Worked with petabyte-scale video and image datasets
• Written processing jobs operating on millions of files
• Debugged distributed system bottlenecks across large fleets of machines

Nice to have:

• Experience streaming dataset formats (e.g. WebDataset)
• Video codec internals and frame-accurate seeking
• Distributed systems experience
• Slurm and Kubernetes for job orchestration
• Experience with object storage performance tuning across providers

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary (SF based role) : $180,000–$300,000 USD + Equity 

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’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.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

You'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't about following established recipes—it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• 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
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

You'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're comfortable debugging distributed training issues and presenting research findings to the team.

You likely have:

• 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
• 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
• Deep understanding of how to effectively evaluate image and video generative models—knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques—FSDP, low precision training, model parallelism—because our models don't fit on one GPU and training decisions impact research outcomes

We'd be especially excited if you:

• Have experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Bring proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

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.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.