Research Scientist – VLM Generalist

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November 14

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Stability AI

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Artificial Intelligence • B2B • SaaS

Stability AI is a company focused on unlocking humanity's potential through generative AI technology. They provide a suite of open models across various modalities, such as image, video, audio, and language, including their powerful model, Stable Diffusion 3. 5. Their models offer high customization, community licensing, and deployment options for developers, making advanced AI accessible for different applications.

51 - 200 employees

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

📋 Description

• Design and fine-tune large-scale VLMs / LLMs — and hybrid architectures — for tasks such as visual reasoning, retrieval, 3D understanding, and embodied interaction. • Build robust, efficient training and evaluation pipelines (data curation, distributed training, mixed precision, scalable fine-tuning). • Conduct in-depth analysis of model performance: ablations, bias / robustness checks, and generalisation studies. • Collaborate across research, engineering, and 3D / graphics teams to bring models from prototype to production. • Publish impactful research and help establish best practices for multimodal model adaptation.

🎯 Requirements

• PhD (or equivalent experience) in Machine Learning, Computer Vision, NLP, Robotics, or Computer Graphics. • Proven track record in fine-tuning or training large-scale VLMs / LLMs for real-world downstream tasks. • Strong engineering mindset — you can design, debug, and scale training systems end-to-end. • Deep understanding of multimodal alignment and representation learning (vision–language fusion, CLIP-style pre-training, retrieval-augmented generation). • Familiarity with recent trends, including video-language and long-context VLMs, spatio-temporal grounding, agentic multimodal reasoning, and Mixture-of-Experts (MoE) fine-tuning. • Awareness of 3D-aware multimodal models — using NeRFs, Gaussian splatting, or differentiable renderers for grounded reasoning and 3D scene understanding. • Hands-on experience with PyTorch / DeepSpeed / Ray and distributed or mixed-precision training. • Excellent communication skills and a collaborative mindset.