Yotta Labs

• Own the full sales lifecycle for both GPU compute and Model API products—from prospecting and qualifying leads to closing deals and expanding long-term partnerships. • Drive GPU compute revenue by identifying organizations with large-scale training, fine-tuning, and inference workloads, positioning Yotta Labs as their preferred infrastructure provider. • Grow Model API adoption by engaging AI application developers, SaaS companies, and enterprises looking for reliable, scalable, and cost-effective API access to leading LLMs. • Develop and execute outbound sales strategies targeting high-value accounts across AI startups, research labs, and enterprise customers scaling AI workloads. • Work closely with marketing to create compelling sales collateral, case studies, and campaigns that highlight our GPU compute and Model API value propositions. • Navigate complex technical and commercial conversations, collaborating with engineering and product teams to tailor solutions to customer requirements. • Consistently meet or exceed revenue and ARR targets, contributing to the growth of a repeatable and scalable sales motion. • Represent Yotta Labs at industry conferences, AI/ML meetups, and customer meetings to grow our presence in the AI infrastructure ecosystem.

Location: Remote (Global) Type: Full-time Company: Yotta Labs Apply: careers@yottalabs.ai 🧠 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. 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