Black Forest Labs

Own complex enterprise sales cycles for generative AI products, from discovery through expansion, while partnering with technical and business stakeholders on APIs, on-premises deployments, and custom licensing. The role requires experience closing technically complex B2B deals and navigating long, multi-stakeholder engagements.

Own the end-to-end developer experience for a generative AI platform, spanning API design, SDKs, dashboards, MCP, and agent-facing surfaces. The role requires 4+ years of software development experience, full-stack capability, and deep expertise building external developer products.

Trains and fine-tunes large-scale diffusion transformer models for image and video generation, conducts rigorous ablation studies, and optimizes distributed training. Requires hands-on diffusion-model experience, strong PyTorch and transformer expertise, and understanding of generative-model evaluation.

The Senior Partnerships Manager will build strategic AI partnerships, develop pricing and licensing models, and translate generative-model capabilities into commercial opportunities. The role requires technical fluency, business development experience in AI or infrastructure, and strong relationship-building skills.

Own field events and partner co-marketing for FLUX, turning enterprise and partner interactions into measurable pipeline. The role requires 3–6 years of B2B technology marketing experience, end-to-end event execution, partner co-marketing, enterprise marketing knowledge, and strong visual and operational skills.

Build frontend-led, full-stack product experiences for generative image and video models, including playgrounds, dashboards, demos, and reference applications. The role requires strong product sense, visual craft, debugging ability, and experience shipping production software while critically directing coding agents.

Leads frontier-scale pretraining research for multimodal image, video, and audio foundation models, shaping architectures, objectives, data strategies, and distributed systems. The role requires prior ownership of production-grade foundation-model pretraining, deep Python and PyTorch expertise, and strong experience with visual generative models.

Owns end-to-end post-training for frontier multimodal generative models, spanning reward modeling, preference optimization, distillation, safety tuning, evaluation, and deployment. The role requires prior experience shipping post-training improvements and strong PyTorch expertise.

Advances vision-language models and integrates multimodal capabilities with FLUX diffusion and flow pipelines. The role requires demonstrated VLM pretraining or substantial architectural advancement, strong research or production results, and multi-node distributed training experience.

The Management Accountant will manage German entity accounting, payroll reconciliations, tax compliance, intercompany transactions, and support US GAAP consolidation across German and US operations. The role requires 3+ years of accounting experience, German and English fluency, and familiarity with German accounting practices and ERP systems.

Owns full sales cycle for AI model API access and licensing to startups and enterprises, managing 30-50+ accounts with focus on retention and expansion. Requires 2-4 years closing technical sales deals, strong outbound skills, and comfort with technical buyers.

Build and optimize high-performance ML inference services and APIs that turn frontier research models (FLUX, Stable Diffusion) into production systems serving millions of requests. Requires experience scaling ML serving infrastructure, GPU optimization, and production backend systems.

Embeds with robotics and physical-AI customers to deploy, fine-tune, and productionize BFL models on real robots. The role combines customer-facing solution delivery with robotics, model serving, backend/frontend engineering, and research prototyping.

Research Engineer focused on optimizing and stabilizing large-scale GPU training for multimodal generative models. The role combines low-level kernel and precision work, distributed training debugging, profiling, benchmarking, and close collaboration with researchers.

Build and operate large-scale research infrastructure for generative AI training, including GPU clusters, distributed systems, telemetry, and reliability tooling. The role requires deep cloud infrastructure expertise, Kubernetes, infrastructure as code, and experience operating large-scale training platforms.