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're looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient training of LLM. Your goal is to make experimentation and training at Inception fast and reliable so our team can focus on science, not system bottlenecks.
What they're looking for
- Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes
- Develop high-performance optimizations to maximize throughput and efficiency
- Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures
- BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience)
- Understanding of ML frameworks (PyTorch, TensorFlow) from a systems perspective
- Strong engineering skills — ability to contribute performant, maintainable code and debug in complex codebases
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're looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient training of LLM. Your goal is to make experimentation and training at Inception fast and reliable so our team can focus on science, not system bottlenecks.
Key Responsibilities
- Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes.
- Develop high-performance optimizations to maximize throughput and efficiency.
- Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
Qualifications
- BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience).
- Understanding of ML frameworks (PyTorch, TensorFlow) from a...
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