Black Forest Labs FLUX

Experience Requirements:

• +3 years of professional work experience

Responsibilities:

• Develop user-facing internal and external applications using Black Forest Labs models and API for various purposes.

• Reason about and develop new user-experiences for next generation AI media models. Build these amazing experiences and neat interfaces in close collaboration with ML research & engineering colleagues.

• Instrument our products with developer-centric tools (e.g. dashboards, evaluation interfaces).

• Maintain and enhance our company website, ensuring it effectively communicates our vision and capabilities; ensure that the website is optimized for performance; testing and debugging the website

Experience Requirements:

• 10 years of relevant FP&A experience, with a very deep mastery of all nuances of accounting, and ideally at a fast growing B2B company

Responsibilities:

• Develop, implement and continuously refine financial models and rolling forecasts to support the company's strategic objectives and ensure flexibility in planning

• Design and implement scalable financial controls and accounting infrastructure

• Lead financial strategy and consolidated reporting across multiple business entities while ensuring regulatory compliance

• Oversee all aspects of financial operations including AR/AP, monthly bookkeeping and reconciliation

• Lead tax and compliance efforts alongside third-party vendors and partnersManage cash flow planning and optimisationPartner with cross-functional teams to provide financial insights that drive business decisionsSupport strategic initiatives, including pricing strategies and longer-term financial planningEstablish efficient processes that can scale with BFL's growth

Responsibilities:

• Develop and maintain scalable infrastructure for large-scale image and video data acquisition

• Manage and coordinate data transfers from various licensing partners

• Implement and deploy state-of-the-art ML models for data cleaning, processing, and preparation

• Implement scalable and efficient tools to visualize, cluster, and deeply understand the data

• Optimize and parallelize data processing workflows to handle billion-scale datasets efficientlyEnsure data quality, diversity, and proper annotation (including captioning) for training readinessGetting training data from alternative sources such as user preferences into trainable formatWork closely in the model development loop to update data as necessitated by the training trajectory