Forward Deployed Machine Learning Engineer

Black Forest Labs•San Francisco, CA

3d•$180,000 - $300,000•Hybrid

About The Position

This role is for a Forward Deployed Machine Learning Engineer who will work at the intersection of cutting-edge research and production reality. The engineer will be responsible for ensuring FLUX models perform optimally in customer environments, architecting deep product integrations, customizing foundation models for visual media, and diagnosing performance bottlenecks. The company has pioneered Latent Diffusion and Stable Diffusion, and their FLUX models power creative tools and design workflows across industries. The team is approximately 50 people and operates from labs in Freiburg and San Francisco.

Requirements

Understand diffusion models not just conceptually, but viscerally—you've debugged them, optimized them, served them at scale.
Experience in the room when a customer's integration goes wrong and you need to diagnose whether it's a model issue, an infrastructure issue, or a fundamental misunderstanding of what the model can do.
Direct experience working with customers on generative AI deployment—the kind where you're iterating on solutions in real-time, not just following a playbook
Hands-on expertise with generative modeling approaches, particularly finetuning, optimizing, and serving deep learning models in production environments
A proven track record as an ML engineer who's shipped models that real systems depend on
Strong Python skills and intuitive understanding of API design (because demos and prototypes are how you communicate what's possible)
The ability to explain why a diffusion model is slow to an executive and how to fix it to an engineer—in the same meeting

Nice To Haves

Deep knowledge of diffusion models and/or flow matching, including finetuning and distillation techniques that go beyond standard tutorials
Know the FLUX ecosystem intimately—ComfyUI, common training frameworks, the tools practitioners actually use
Battle-tested experience optimizing inference for transformer-based models (and the scars to prove it)
Can architect solutions in complex enterprise environments where "just add more GPUs" isn't an option
Contribute to open-source projects in the diffusion model space and understand the community
Deployed models on cloud platforms using state-of-the-art serving infrastructure

Responsibilities

Ensures FLUX models perform optimally in customer environments—whether that's on-premise GPU clusters or BFL-hosted infrastructure—balancing the eternal tension between latency and output quality
Architects deep product integrations that go far beyond "here's an API endpoint"—helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet
Customizes foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible
Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)
Discovers where generative visual AI should go next by understanding what industries are struggling with problems we could solve