We are leveraging diffusion technology to develop a new generation of LLMs. Our dLLMs are much faster and more efficient than traditional autoregressive LLMs. Backed by AI Grant and Amplify.
About the role
We seek experienced scientists and engineers with deep expertise in pre-, mid-, and post- training large language models. You will advance our diffusion-based LLM models, developing novel training techniques and pushing the boundaries of parallel token generation.
What they're looking for
- Design, develop, and optimize architectures for diffusion-based language models
- Implement innovative training objectives and loss functions for discrete diffusion LLMs
- Research and implement techniques for controlled text generation and constraint satisfaction
- Develop methods for multi-modal integration within the diffusion framework
- Improve model efficiency, reduce training time, and optimize inference throughput
- Develop and implement post-training techniques to align and improve model behavior
More about this role
Inception creates the world’s fastest, most efficient AI models. Our Mercury model is the world’s fastest reasoning LLM and first commercially available diffusion LLM, delivering 5x greater speed and efficiency than today’s LLMs, with best-in-class quality.
We are the AI researchers and engineers behind such breakthrough AI technologies as diffusion models, flash attention, and DPO.
The Role
We seek experienced scientists and engineers with deep expertise in pre-, mid-, and post- training large language models. You will advance our diffusion-based LLM models, developing novel training techniques and pushing the boundaries of parallel token generation.
Key Responsibilities
- Design, develop, and optimize architectures for diffusion-based language models.
- Implement innovative training objectives and loss functions for discrete diffusion LLMs.
- Research and implement techniques for controlled text generation and constraint satisfaction.
- Develop methods for multi-modal integration within the diffusion framework.
- Improve model efficiency, reduce training time, and optimize inference throughput.
- Develop and implement post-training techniques to align and improve model...
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