Machine Learning Engineer - Moderation

About Black Forest Labs

We’re the team building the foundational layer for visual intelligence: multimodal models trained jointly on images, video, audio, and action to see, generate, and understand the world.

Our founding team pioneered latent diffusion and Stable Diffusion, the breakthroughs that made generative AI accessible to millions. With FLUX, we're pushing into generative media at scale, machines that act in the physical world, and emerging applications in computer use.

Our FLUX models top public benchmarks and run in production in some of the world's biggest platforms. We're a small, fast growing research lab competing with the largest companies in the world. From our labs in Freiburg to San Francisco, we're building what comes next.

Why This Role

Black Forest Labs’ products arm individuals with great creative power to produce novel media across a range of modalities. The quality of our models inevitably also gives bad actors the opportunity to create harmful, copyright-infringing, or illegal content.

In this role you will develop and improve our moderation system: a multilayered stack of detection models. Succeeding in this role requires creativity and familiarity with a large range of ML techniques. An effective solution that balances accuracy, cost, latency, and scalability will vary case-by-case, from classical ML fine-tuning, to RAG-based agents, to simple, lightweight semantic embeddings.

This is a Senior / Staff IC role. Our ideal candidate is able to work across the full stack of ML engineering, collaborate cross-functionally with many teams, including legal, policy, go-to market, and infra, and has a real interest in the domains of AI safety and integrity.

What You’ll Work On

• Develop ML models and systems to detect harmful and/or illegal content across a variety of classes and modalities

• Collaborate with our core infrastructure team to integrate these models into a large-scale production serving pipeline

• Handle customer requests and feedback on our moderation behavior, and make quick adjustments in response

• Collaborate with our legal, policy, and product teams to design moderation policies that are technically feasible and effective while preserving a positive user experience

What We’re Looking For

• Experience deploying VLMs at scale for the classification of multimodal content

• Experience building AI systems around state-of-the-art open-source VLMs

• Experience tuning models for classification tasks, including building eval sets, reasoning about precision/recall at threshold, and monitoring for drift

• Comfort with the full stack of ML engineering: from research and experimentation to large-scale deployment

Nice to have:

• Comfort with the adversarial nature of generative AI moderation: prompt obfuscation, jailbreaks, output laundering, and evasion generally

• Experience in moderation/safety space, preferably across a range of modalities (text, image, video, audio)

• Experience optimizing VLM inference latency at scale

• Experience with non-VLM solutions for safety classification (e.g., classical ML)

• Domain knowledge and experience with content copyright and/or child safety moderation systems

How We Work Together

We’re a distributed team with real offices that people actually use. This role is based in San Francisco, and you’ll join us in the office at least 2 days a week, or one full week every other week.

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:

US $220,000 - $280,000 USD + Equity

Note: We may use AI-assisted tools to help organize applications. All hiring decisions are always made by our team.