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 reinforcement learning for large models. This role sits at the intersection of research and large-scale systems engineering: you'll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers to make RL stable, fast, and production-ready.
What they're looking for
- Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads
- Improve the reliability and scalability of RL training pipelines, distributed RL workloads, and training throughput
- Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems
- BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience)
- Understanding of ML frameworks (PyTorch, TensorFlow, Ray, Megatron) from a systems perspective
- Experience working with reinforcement learning workloads (PPO, DPO, RLHF, or reward modeling)
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 reinforcement learning for large models. This role sits at the intersection of research and large-scale systems engineering: you'll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers to make RL stable, fast, and production-ready.
Key Responsibilities
- Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
- Improve the reliability and scalability of RL training pipelines, distributed RL workloads, and training throughput.
- Develop shared monitoring and observability tools to ensure high uptime,...
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