GPU Cloud Platform Engineer

Job details

Work model

Remote

Salary

Not listed by source

Location

United States

Hiring context

How this role compares at Yotta Labs

Yotta Labs has 4 live roles in MeritLog’s catalog across 2 job families, and 3 of them are in engineering. 0 of those listings publish a pay range, a disclosure rate of 0%.

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What the role asks for

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

Location: Remote (Global) Type: Full-time Company: Yotta Labs Apply: careers@yottalabs.ai 🧠 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 - Build and operate large-scale, high-performance GPU clusters; ensure stable 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., node affinity, taints/tolerations) to improve GPU resource utilization. - Build a unified multi-cluster management and monitoring system to support cross-region resource monitoring, traffic scheduling, and fault failover. Collect key metrics such as GPU memory usage, QPS, and response latency in real time; configure alert mechanisms. - Coordinate with IDC providers for planning and deploying large-scale GPU clusters, networks, and storage infrastructure to support internal cloud platforms and external customer needs. ✅ Qualifications - Bachelor's degree or higher in Computer Science, Software Engineering, Electronic Engineering, or related fields; 3+ years of experience in system engineering or DevOps. - 5+ years of experience in cloud-native development or AI engineering, with at least 2 years of hands-on experience in Kubernetes multi-cluster management and orchestration. - Familiarity with the Kubernetes ecosystem; hands-on experience with tools such as kubectl, Helm, and expertise in multi-cluster deployment, upgrade, scaling, and disaster recovery. - Proficient in Docker and containerization technologies; knowledge of image management and cross-cluster distribution. - Experience with monitoring tools such as Prometheus and Grafana; Has practical experience in GPU fault monitoring and alerting. - Hands-on experience with cloud platforms such as AWS, GCP, or Azure; understanding of cloud-native multi-cluster architecture. - Experience with cluster management tools such as Ray, Slurm, KubeSphere, Rancher, Karmada is a plus. - Familiarity with distributed file systems such as NFS, JuiceFS, CephFS, or Lustre; ability to diagnose and resolve performance bottlenecks. - Understanding of high-performance communication protocols such as IB, RoCE, NVLink, and PCIe. - Strong communication skills, self-motivation, and team collaboration 🌟 Preferred Experience - Experience in developing and operating MaaS platforms or large-scale model inference clusters. Proven track record of leading multi-cluster system development or performance optimization projects. - Proficiency in CUDA programming and the NCCL communication library; understanding of high-performance GPUs like H100. - Ability to develop standardized inference APIs (RESTful/gRPC) and automation tools using Golang or Python. - Hands-on experience with optimization techniques such as model quantization, static compilation, and multi-GPU parallelism; capable of profiling inference processes in multi-cluster setups and identifying bottlenecks like memory fragmentation and low compute efficiency. - Active engagement with open-source communities such as Hugging Face and GitHub; deep understanding of the design principles of inference frameworks like Triton, vLLM, and SGLang; ability to perform secondary development and optimization based on open-source projects and quickly translate cutting-edge techniques into production-ready multi-cluster solutions. 🌐 Why Join Yotta Labs? - Be part of a visionary team aiming to redefine AI infrastructure. - Work on cutting-edge technologies that bridge AI and decentralized computing. - Collaborate with experts from leading institutions and tech companies. - Enjoy a flexible, remote work environment that values innovation and autonomy. 📩 How to Apply Interested candidates should apply directly or send their resume and a brief cover letter to careers@yottalabs.ai. Please include links to any relevant projects or contributions.