4 of 4 Permanent Stable Diffusion Jobs in Cambridge

Our topics of interest include but are not limited to: Contrastively-trained and auto-regressive Vision & Language (e.g. CLIP, BLIP). Visual LLMs (e.g. LLaVA). Generative Models (e.g. Stable Diffusion and Auto-Regressive models). Efficient Architectures. Model Compression (distillation, quantization). Efficient Adaptation of Large Models. Key Responsibilities: Conduct hands-on innovative research, including methodological conceptualization More ❯

Our topics of interest include but are not limited to: Contrastively-trained and auto-regressive Vision & Language (e.g. CLIP, BLIP). Visual LLMs (e.g. LLaVA). Generative Models (e.g. Stable Diffusion and Auto-Regressive models). Efficient Architectures. Model Compression (distillation, quantization). Efficient Adaptation of Large Models. Key Responsibilities: Conduct hands-on innovative research, including methodological conceptualization More ❯

Our topics of interest include but are not limited to: Contrastively-trained and auto-regressive Vision & Language (e.g. CLIP, BLIP). Visual LLMs (e.g. LLaVA). Generative Models (e.g. Stable Diffusion and Auto-Regressive models). Efficient Architectures. Model Compression (distillation, quantization). Efficient Adaptation of Large Models. Key Responsibilities: Conduct hands-on innovative research, including methodological conceptualization More ❯

Our topics of interest include but are not limited to: Contrastively-trained and auto-regressive Vision & Language (e.g. CLIP, BLIP). Visual LLMs (e.g. LLaVA). Generative Models (e.g. Stable Diffusion and Auto-Regressive models). Efficient Architectures. Model Compression (distillation, quantization). Efficient Adaptation of Large Models. Key Responsibilities: Conduct hands-on innovative research, including methodological conceptualization More ❯