Forward Deployed Machine Learning Engineer
Vor 4 Tagen
Black Forest Labs is a cutting-edge startup pioneering generative image and video models. Our team, which invented Stable Diffusion, Stable Video Diffusion, and FLUX.1, is currently looking for a Forward Deployed MLE to help us take our core offerings and expertise to the hands of our critical customers.
We are looking for candidates with a strong understanding and a clear grasp of the inner workings of generative models - diffusion models especially - as well as prior hands-on experience in inference optimization. Additionally some experience serving these models via APIs would be a bonus. You should enjoy working with customers, better understand their needs and assist them with BFL models such as help with finetuning FLUX models for their specific use case.
The Role
As a Forward Deployed Machine Learning Engineer for BFL you will:
- Help customers to set up FLUX models in their services and ensure an optimal performance in terms of latency and output quality, both for on premise deployments and BFL-hosted integrations.
- Design and implement deep product integrations for customers, and help them with model hosting, finetuning, deployment and inference optimization
- Customize foundation models for visual media for customer specific use cases
- Meet with customers to identify gaps and pain points, assess their needs for BFL's solutions, and help them to decide between BFL-hosted and on premise deployments
- Collaborate with customers to identify relevant use cases and find innovative solutions for solving these
- Identify expansion potentials for BFL's technologies into new industry sectors across the globe
Ideal experiences
- Experience in working directly with customers, iterating on solutions and providing tailored support for serving generative AI models
- Prior experience with common generative modeling approaches and hands-on experience with finetuning, optimizing and serving deep learning models.
- Proven track record in machine learning engineering
- Proficiency in Python and understanding of API integrations to implement basic functionality and help customers with prototyping/demo building.
- Experience in explaining and summarizing sophisticated technical concepts to both technical and non-technical audiences
- Excellent communication skills and experience in collaborating with non-technical stakeholders internally and externally, and the ability to adapt messaging to different audiences and explain complex technical concepts in simple terms with technical and non-technical audiences
Nice to have
- Prior knowledge about diffusion models and/or flow matching, and relevant finetuning and distillation techniques
- Experience in using common open source tooling around from the FLUX ecosystem such as ComfyUI and common trainer frameworks
- Experience with inference optimizations for transformer based machine learning models
- Proven ability to architect solutions in complex enterprise environments
- Contributed to open-source projects in particular in the space of diffusion models
- Experience with cloud platforms and state of the art deployment solutions for diffusion models
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