Staff Software Engineer (AI Compute, Together Cloud)

Join Together Cloud as a Staff Software Engineer, where you'll set the technical direction and build major components of the next generation AI cloud platform. This architect-and-build role involves owning the GPU and network virtualization stack, the in-DC IaaS layer, and designing the GPU scheduling and global management plane. You'll also be responsible for architecting monitoring and automated remediation for fault tolerance, setting technical direction across teams, growing the team, and setting the engineering bar.

- Set technical direction and build major components of the next generation AI cloud platform, focusing on virtualization of the latest ML hardware. - Architect and roadmap the services, Kubernetes operators, and libraries that provision and manage compute, storage, and networks in data centers. - Lead design reviews, resolve cross-cutting architectural disagreements, and define the standards other engineers build against.

- Demonstrated technical leadership beyond your own commits: mentoring senior engineers, leading design reviews, and driving alignment across teams that do not report to you - Experience building and operating reliable, customer-facing production systems at scale, and owning the infrastructure automation (Terraform, Ansible), observability (Prometheus, Grafana), and CI/CD (GitHub Actions, ArgoCD) that keep them healthy - Expert systems knowledge across compute, networking, and storage — including concurrency, memory management, performant I/O, and scale at a global level - 7+ years of professional software development experience, with expert-level proficiency in at least one backend language (Golang desired), writing high-performance, well-tested, production-quality code - Excellent communication and diplomacy skills — able to write design docs that settle arguments, and to work effectively with technical and non-technical stakeholders - Deep experience building and operating globally distributed, high-performance microservice architectures across one or more cloud providers (AWS, Azure, GCP) - Track record of owning the architecture of large distributed systems from blank page to production at scale, including the judgment calls that could not be reversed cheaply - Deep Kubernetes internals experience, such as implementing non-trivial Kubernetes operators, device/storage/network plugins, custom schedulers, or patches to Kubernetes itself - Deep experience with VMs/hypervisors, such as QEMU/KVM, cloud-hypervisor, VFIO, virtio, PCIE passthrough, Kubevirt, SR-IOV - Deep experience with DC networking tech + solutions, such as VLAN, VXLAN, VPN, VPC, OVS/OVN - Experience working on high-performance compute, networking, and/or storage - Experience with Cluster API or similar - Experience virtualizing GPUs and/or InfiniBand - Experience building IaaS or PaaS systems at scale - Experience with DPUs/SmartNICs - GPU programming, NCCL, CUDA knowledge