Member of Technical Staff - VLM
Advances vision-language models and integrates multimodal capabilities with FLUX diffusion and flow pipelines. The role requires demonstrated VLM pretraining or substantial architectural advancement, strong research or production results, and multi-node distributed training experience.
About the job
Responsibilities
- Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack, innovating on architectures rather than only applying existing ones.
- Design fine-tuning strategies for specialized creative use cases, including captioning, editing instructions, and prompt enhancement.
- Research integrations between VLM/LLM capabilities and diffusion and flow pipelines to improve generation quality and controllability without computational bottlenecks.
- Evaluate emerging multimodal architectures and translate recent research into practical improvements.
Requirements
- Pretrained or significantly advanced a VLM, beyond SFT or LoRA fine-tuning, that was deployed in production or released publicly.
- Strong publication record or an unambiguous production track record demonstrating work on multimodal architectures.
- Deep understanding of vision-language representation interaction, including tokenization, alignment, grounding, cross-modal attention, and failure modes.
- Experience with distributed training at multi-node scale.
- Comfortable working at the research/production boundary, with a focus on shipping and generalization.
Nice-to-haves
- Experience with diffusion or flow-based generative models, especially combining autoregressive and diffusion paradigms.
Compensation
- Base annual salary: €130,000–€340,000
- Equity
Skills
Vision-Language Models, Multimodal Architectures, LLMs, Diffusion Models, Flow-Based Models, Distributed Training, Multi-Node Training, Tokenization, Cross-Modal Attention, Grounding, Model Fine-Tuning, Python
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