Strategic Finance/FP&A

Overview

What if financial planning could move as fast as AI research? Our founding team pioneered Latent Diffusion and Stable Diffusion - breakthroughs that made generative AI accessible to millions. Today, our FLUX models power creative tools, design workflows, and products across industries worldwide. Our FLUX models are best-in-class not only for their capability, but for ease of use in developing production applications. We top public benchmarks and compete at the frontier - and in most instances we're winning. If you're relentlessly curious and driven by high agency, we want to talk. With a team of ~50, we move fast and punch above our weight. From our labs in Freiburg - a university town in the Black Forest - and San Francisco, we're building what comes next.

What You'll Pioneer

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.

• 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

Questions We're Wrestling With

• How do you forecast revenue when your models are simultaneously open source and commercial, free tier and enterprise?
• What's the right balance between investing in cutting-edge research (expensive, uncertain) and scaling known winners (profitable, competitive)?
• What do healthy gross margins look like when your COGS is GPU compute and infrastructure optimization is a competitive advantage?
• How do you plan headcount when breakthrough hires in research might exponentially increase capabilities?
• How do you structure pricing as customers move from experimentation to production at scale?
• What financial frameworks work for a German company scaling globally with US operations?

Who Thrives Here

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.

• 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

What We're Building Toward

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.