Bring newly racked CPU and GPU servers from hardware handoff through full provisioning and configuration to production readiness.
Configure out-of-band management, perform PXE booting and OS provisioning, and troubleshoot common imaging failures.
Maintain inventory accuracy, coordinate with upstream teams to resolve blockers, and follow established runbooks for process improvement.
Vultr makes high-performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators worldwide. As the world's largest privately-held cloud infrastructure company with a $3.5 billion valuation, we serve hundreds of thousands of customers across 185 countries.
Lead the architecture and delivery of a large-scale GPU infrastructure platform, evolving from managed Kubernetes to bare-metal with Slurm and inference support.
Manage a distributed engineering team across backend, frontend, DevOps, QA, and documentation, setting technical standards and overseeing implementation.
Own GPU infrastructure operations, including Slurm, Kubernetes, NVIDIA hardware, observability, and incident response, while acting as the primary technical interface with partners.
Jobgether is an AI-powered job matching platform that connects candidates with relevant roles, ensuring a fair and objective review process. It operates with a distributed team and partners with companies globally, focusing on efficient and transparent recruitment.
Architect GPU cluster topologies spanning compute, networking, storage, and control planes.
Model AI workloads like LLM training and inference to guide performance tradeoffs.
Collaborate with cross-functional teams to deploy and scale reliable AI infrastructure.
This role is with a partner company focused on designing next-generation AI infrastructure at massive scale. The company fosters a fast-moving, engineering-led culture with a collaborative, international team.
Lead end-to-end technical deployments for GPU neocloud and AI Factory customers.
Configure and troubleshoot bare metal GPU infrastructure including CNI, GPU Operator, and storage.
Build playbooks and transfer knowledge to customer teams for self-sufficiency.
vCluster Labs provides a platform for AI infrastructure, helping GPU cloud providers and enterprises build Kubernetes-based clusters. They are a venture-backed startup with 40+ engineers and a remote-first culture, powering over 100,000 GPUs.
Bring new open-source models into production with a focus on latency and throughput.
Optimize inference systems including batching, KV cache, speculative decoding, and quantization.
Design, deploy, and operate heterogeneous clusters across infrastructure vendors to improve reliability and cost efficiency.
They are an early-stage AI infrastructure startup making large language model inference faster and more cost-efficient. The small team focuses on low-level performance engineering and AI-assisted workflows, with a strong emphasis on technical fundamentals.