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About Yotta Labs
Yotta Labs is pioneering the development of a Decentralized Operating System (DeOS) for AI workload orchestration at a planetary scale. Our mission is to democratize access to AI resources by aggregating geo-distributed GPUs, enabling high-performance computing for AI training and inference on a wide spectrum of hardware—from commodity to high-end GPUs. Our platform supports major large language models (LLMs) and offers customizable solutions for new models, facilitating elastic and efficient AI development.
Role Overview
We are seeking a GPU Cloud Platform Engineer to join our core infrastructure team and help build the next-generation AI compute cloud. In this role, you will design, deploy, and operate large-scale, multi-cluster GPU infrastructure across data centers and cloud environments. You will be responsible for ensuring high availability, performance,
and efficiency of containerized AI workloads—ranging from LLMs to generative models—deployed in Kubernetes-based GPU clusters.
Responsibilities
- Build and operate large-scale, high-performance GPU clusters; ensure secure operation of compute, network, and storage systems; monitor and troubleshoot online issues.
- Conduct performance testing and evaluation of multi-node GPU clusters using standard benchmarking tools to identify and resolve performance bottlenecks.
- Deploy and orchestrate large models (e.g., LLMs, video generation models) across multi-cluster environments using Kubernetes; implement elastic scaling and cross-cluster load balancing to ensure efficient service response under high concurrency for global users.
- Participate in the design, development, and iteration of GPU cluster scheduling and optimization systems. Define and lead Kubernetes multi-cluster configuration standards; optimize scheduling strategies (e.g.,