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 post-training large language models through reinforcement learning. You will design and implement RL training pipelines for our diffusion LLMs, develop reward modeling strategies, and build the algorithms that align model behavior with human intent at scale.
What they're looking for
- Design, develop, and optimize RL training pipelines (PPO, DPO, RLHF, and novel approaches) for diffusion-based LLMs
- Build and iterate on reward models, reward shaping strategies, and evaluation of reward quality
- Implement innovative approaches for fine-tuning and scaling generative AI models
- Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases
- Research and implement techniques for controlled text generation and constraint satisfaction
- Improve training stability, efficiency, and reproducibility of RL workloads
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 post-training large language models through reinforcement learning. You will design and implement RL training pipelines for our diffusion LLMs, develop reward modeling strategies, and build the algorithms that align model behavior with human intent at scale.
Key Responsibilities
- Design, develop, and optimize RL training pipelines (PPO, DPO, RLHF, and novel approaches) for diffusion-based LLMs.
- Build and iterate on reward models, reward shaping strategies, and evaluation of reward quality.
- Implement innovative approaches for fine-tuning and scaling generative AI models.
- Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
- Research and implement techniques for controlled text...
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