At Verda, we're building a full-stack AI cloud, covering everything from data centers and hardware to our own cloud platform that the world's leading AI teams use to do serious AI work. We strive to make a positive mark on the world through the infrastructure we build and give leading teams a service they can truly depend on. Headquartered in Helsinki, we operate globally with offices in London and San Francisco. Cash and equity compensation along with local benefits. A real chance to make an impact and work alongside world class engineers, researchers, and partners across the integral AI ecosystem. A senior team that is growing, and a leadership role in defining its structure and practices. Hybrid work from our Helsinki/ London office or fully remote work within EU. It runs Ceph across every Verda datacenter: block storage (RBD) behind every VM volume, CephFS and NFS behind our Shared File System, and RGW behind S3-compatible Object Storage, which is launching to customers with multi-site replication. The team operates shared cloud clusters and dedicated storage clusters for customers running large-scale ML training, brings up a new storage cluster with every datacenter we open, and provides storage to internal engineering teams, including the Kubernetes platform. It also owns storage performance and capacity, the shared-filesystem strategy for AI workloads (including evaluation of high-performance parallel filesystems), security response for the storage stack, and 24/7 storage on-call. Ceph (RBD, CephFS, RGW multisite), NVMe, HAProxy, Ubuntu, containers, high speed fabric storage networks, Gitlab, VictoriaMetrics, Grafana, PagerDuty. You will lead the Storage team as its Engineering Manager,



reporting to the VP of Infrastructure. The team is small, senior, and growing as Verda opens new datacenters at a rate of greater than one a month. Your job is to turn deep individual expertise into a mature team that can scale: you own its people, its delivery, and the reliability and durability of the data it holds. You own priorities and projects. You set the priorities, give requests a clear front door, say "not yet" when it's the right answer, and drive multi-quarter projects to completion. A storage incident can mean lost data and lost customer trust, and you are accountable for it not happening. This role requires real storage engineering depth. Manage the Storage team directly: 1:1s, coaching, career development, performance, and compensation input. Spread storage knowledge through pairing, internal training and documentation, so every subsystem has more than one person who can own it at 3am. Establish clear ownership areas within the team across block storage and VM volumes, shared filesystems, object storage, dedicated customer clusters, new-site buildout, and performance and capacity.
Own the storage security and compliance posture with the Security team: vulnerability response, encryption, data lifecycle and sanitisation, and customer-data handling rules.



Introduce lightweight triage and prioritisation so requests from Cloud, Provisioning, Platform, AI/ML and customer-facing teams have a clear front door and unplanned work is visible and managed. Represent the team to Infrastructure peers (SRE, Provisioning, Network, Platform, Observability), to product teams, and to customers. Work with DC Ops and procurement on capacity planning, hardware and vendor evaluations, turning a technical recommendation into a decision with cost, risk and timeline attached. Projects 3+ years managing infrastructure or software engineers, including hiring, performance management, and leading a team through significant growth or change. RBD vs CephFS vs NFS vs S3, replication vs erasure coding, what drives latency and throughput, and how storage interacts with the network. Linux systems and performance depth: NVMe, filesystems, storage networking (MTU, bonding), and performance analysis. A track record of taking a team from heroics to process: runbooks, change management, planning and metrics. Ceph RGW multisite, S3 compatibility, and running object storage as a customer-facing product. High-performance shared or parallel filesystems for AI: Lustre, VAST, DDN, WEKA, GPUDirect Storage, RDMA or NVMe-oF. Storage for GPU clusters and ML training or inference (checkpointing, dataset throughput), including NVIDIA reference architectures. Storage for virtualisation (QEMU/KVM) and Kubernetes (CSI, Rook). Vendor management and structured hardware or software evaluations. Storage security and compliance: encryption at rest, data sanitisation, ISO * or SOC 2. #

📌 Engineering Manager Data Services Remote in Remote (España)
🏢 Verda
📍 España