Requirements

You've pretrained or significantly advanced a VLM (not just SFT'd or LoRA'd one) that was deployed in a production system or released publicly
Strong publication record or unambiguous production track record showing you push the frontier on multimodal architectures
Deep understanding of how vision and language representations interact: tokenization, alignment, grounding, cross-modal attention, and the failure modes of each
Experience with distributed training at multi-node scale
Comfortable at the research/production boundary — you care whether the work ships and generalizes, not just whether it reads well
Experience with diffusion or flow-based generative models is a strong plus — especially if you've thought about how autoregressive and diffusion paradigms can compose

What You'll Be Doing

Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack — innovating on architectures, not just applying existing ones
Design fine-tuning strategies that adapt VLMs to specialized creative use cases (captioning, editing instructions, prompt enhancement) that general-purpose models can't handle
Research integrations between VLM/LLM capabilities and our diffusion and flow pipelines — finding creative ways to improve generation quality and controllability without computational bottlenecks
Evaluate emerging multimodal architectures, translating the best of recent research into practical improvements