Applied Research Engineer, Evaluation

About this role

About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and videos. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role Frontier image and video models are now good enough that the hard question is no longer whether they work, but whether a new one is actually better, and how you'd know. Aesthetics, prompt following, editing fidelity, text rendering, character consistency: none of these collapse into a single number, and public benchmarks have fallen well behind the models they're meant to measure. You'll work on the system BFL uses to answer that question.

Every FLUX checkpoint, every finetuning and RL run, and every competitor release goes through our evaluation stack, and you'll help build and sharpen it. Product Research relies on what it shows to decide what to fix next, and to confirm the fix worked. It gets harder from here. FLUX 3 is our first multimodal model, spanning image, video, audio and action, and each new modality needs its own way of being measured. What You'll Work On What you focus on will depend on where you're strongest and what the team needs most when you join.

In scope: Building and maintaining the evaluation stack that every model checkpoint has to pass through and prove itself on, from automated benchmarking and LLM-as-judge pipelines, to human evaluation through panels (Prolific and otherwise). Designing the breadth of evaluation across the taxonomy, from prompt adherence, editing fidelity, aesthetics, text rendering, subject and character consistency, to safety. Evaluating the evaluation stack itself: checking that human and automated evals agree, that results stay stable over time, and improving them when they don't.

Benchmarking FLUX against the frontier models continuously, while obtaining a meaningful perspective on where we are better or worse than competitors and what such differences really mean. Finding the ways in which the quality of the model's output falls short that are not captured by aggregate numbers. Closing the loop, ideally, with the RL and finetuning researchers, by defining target metrics for them to aim for, and verifying that a new checkpoint improved the weakness without introducing new ones.