Responsibilities:

• Own DevExp as a product surface - wherever the bottleneck is, that's where you ship next.
• 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.
• 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.
• 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.
• Design the reference patterns and SDK primitives developers reach for when building agentic workflows on FLUX.
• Stress-test every API and SDK change against 'would Claude / GPT / an autonomous agent get this right on the first try?' - and treat agents as a first-class user across the whole product.

Requirements:

• 4+ years building software, with meaningful time on developer-facing products - SDKs, APIs, dashboards, consoles, platforms developers actually use.
• Full-stack comfortable. TypeScript / React / Next on the front, Python (and ideally Go or Rust) on the back.
• Deep instincts on API design - you have opinions about error formats, pagination, async job shapes, breaking changes, and you've lived with the consequences of bad ones.
• 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.
• You've shipped developer-facing product that real external developers depend on. Not internal tooling.

Nice to Have:

• Background at a developer platform where DevExp was a top-level metric (Stripe, Vercel, Cloudflare, Modal, Replicate, Resend, Linear, Anthropic platform, etc.).
• You've built or meaningfully contributed to an MCP server real users / agents rely on.
• 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).
• A public surface - OSS, a writeup, a dashboard you're proud of - that lets us see your taste before the interview.

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

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:

• Research checkpoints sit longer before becoming usable APIs
• Inference is slower than it needs to be
• APIs struggle under load
• Demos don’t reflect the true potential of our models

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.

What You’ll Work On

You will own the bridge between research breakthroughs and production systems.

• Turn research checkpoints into production-ready inference services
• Design and maintain high-performance APIs serving millions of requests
• Optimize inference latency and throughput across GPU infrastructure
• Build scalable serving architectures that handle unpredictable traffic
• Improve reliability, monitoring, and observability across model-serving systems
• Prototype and ship demos that showcase new capabilities in days, not weeks
• Collaborate closely with researchers to move from idea to live endpoint rapidly

Tools & Context

– Model Serving & API Infrastructure

• Python, FastAPI, async systems
• GPU infrastructure, CUDA, inference optimization
• Docker and Kubernetes
• Redis, Postgres, distributed task queues
• Cloud platforms (AWS, GCP, or Azure)
• Observability stacks (metrics, logging, tracing)

This role spans backend systems, GPU performance, and production ML serving.

What We’re Looking For

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:

• Strong judgment around performance, reliability, and cost tradeoffs
• Experience scaling APIs or ML systems under load
• Comfort working in fast-moving, research-adjacent environments
• Ownership from system design through debugging and deployment

Role-specific experience we value:

• Building and operating ML inference services in production
• Designing scalable API architectures with async processing
• Optimizing GPU workloads (batching, quantization, compilation, CUDA)
• Managing distributed systems and task queues under variable load
• Implementing monitoring and observability for production ML systems
• Debugging performance bottlenecks across model, infrastructure, and network layers

Bonus experience includes:

• Real-time or low-latency inference systems
• TensorRT, reduced precision, layer fusion, or model compilation techniques
• Frontend demo tooling (Streamlit, Gradio, React)
• CI/CD and automated testing for ML systems
• Security best practices for API and model serving

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.

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.

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

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We'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.

Responsibilities

• 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

Requirements

• 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.

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We'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'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet.

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.

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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, shaping training objectives, architectures, data strategies, and systems behind our joint image, video, and audio foundation models.

Responsibilities

• 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

Requirements

• 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.

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This is a full-cycle role, and you will be responsible for everything from first touch to close to renewal and upsell. You will work with a variety of accounts, including fast-moving AI startups and household-name enterprises. You will need to be able to land deals, prove value, and grow accounts 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 for you.

We're looking for someone who has closed real deals, moves fast, and is hungry to have outsized impact at a company where your work directly drives revenue. You should have 2-4 years of experience in a closing sales role, ideally with a BDR-to-AE progression. You should be comfortable discussing model capabilities, API integration, and deployment with technical buyers. You should have a track record of managing a high volume of accounts and deals in parallel without dropping balls. You should be organized, disciplined, and self-directed.

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Key responsibilities include:

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

Technical focus includes:

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

Requirements include:

• 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

We offer a competitive base annual salary of $180,000-$300,000 USD and a hybrid work model with a meaningful in-person presence.

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Member of Technical Staff - Image / Video Generation

Job Description

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.

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.

Required Skills

• 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

Preferred Skills

• Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• 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.

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