Full-time GPU Cloud Platform Engineer on Yotta Labs' core infrastructure team, building and operating large-scale multi-cluster GPU infrastructure across data centers and clouds. Work covers Kubernetes-based orchestration of containerized LLM and generative model workloads, scheduling, monitoring, and failover. Stack includes Kubernetes, Helm, Docker, Prometheus, Grafana, Ray, Slurm, and NVIDIA/AMD/AWS Trainium hardware.
Location: Remote (Global)
Type: Full-time
Company: Yotta Labs
Apply: [email protected]
🧠 About Yotta Labs
Yotta Labs is building the next generation multi-silicon AI cloud and runtime platform to power the world’s most demanding AI workloads. We enable training and inference across NVIDIA GPUs, AMD GPUs, and AWS Trainium, helping AI companies achieve the best performance and economics across heterogeneous hardware. Our mission is to provide high-performance AI computing and Model API services, enabling AI companies, research labs, and enterprises to train, deploy and integrate cutting-edge models at scale.
🛠️ 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. If you're passionate about high-performance systems, distributed orchestration, and scaling real-world AI infrastructure, this role offers a unique opportunity to shape the backbone of our AI cloud platform.
🎯 Responsibilities
✅ Qualifications
🌟 Preferred Experience
🌐 Why Join Yotta Labs?
📩 How to Apply
Interested candidates should apply directly or send their resume and a brief cover letter to [email protected]. Please include links to any relevant projects or contributions.