Volta is a French–Italian AI-powered platform that digitises and automates B2B sales operations for suppliers and distributors.
ABOUT THE ROLE Volta builds and operates large scale GPU compute infrastructure for AI workloads. Our platform is Kubernetes-native, spans multiple regions, and delivers virtual machines, storage, and networking through a fully automated infrastructure stack. Platform Engineering is organized as capability teams, each owning a defined part of the stack: compute, storage, the control plane and API layer, confidential computing, networking, and the customer-facing surface. You will lead one of them. This is a hands-on leadership role. You set technical direction for your team's area, carry accountability for what it delivers, and manage the people doing the work. You are expected to stay close enough to the code to take on the hard problems yourself. WHAT YOU WILL BE DOING - Lead a platform engineering team: technical direction, design reviews, code review, and day-to-day delivery. - Set the technical direction for your team's area of the stack, and design and build the platform layers it owns as product requirements take shape. - Represent platform engineering in roadmap planning: translate product requirements into scoped, sequenced technical work, communicate trade-offs, and keep delivery on track. - Work closely with the bring-up teams to surface operational pain points and turn them into scalable platform features. - Collaborate with the security engineering team to integrate security guidance at the platform layer and ensure findings are remediated effectively. - Coordinate with the other platform engineering leads on shared architecture, joint initiatives, and cross-team and cross-timezone delivery. - Own reliability, observability, security, and interface and versioning standards for the services your team ships. - Stay hands-on: take on complex or high-risk engineering work directly alongside the team. - Run post-mortems and close structural gaps after incidents, not just the immediate issue. - Manage the team: 1:1s, performance conversations, hiring interviews, and onboarding. WHAT YOU BRING - 5+ years in cloud platform or infrastructure engineering, with at least 2 years leading an engineering team. - Strong backend or systems programming background in a production environment. Our working languages are Python, Go, and Rust; we welcome strong engineers from other compiled or object-oriented languages (for example C++, C#, or Java) who are ready to work across our stack as it evolves. - Kubernetes at depth: cluster operations, CNI networking, scheduling, and workload management in production. - GPU infrastructure experience: provisioning, resource allocation, and surfacing capacity through a platform or API layer. - Solid networking fundamentals: L2/L3, VLANs, overlay networks, and multi-tenant isolation in cloud environments. - Service architecture skills: designing, operating, and versioning production-grade control-plane services and APIs across IaaS and above, holding the team to clean, industry-standard interface and breaking-change practices. - Security awareness: trust boundaries, least privilege, and secure defaults when building platform services. - Proven people management: running 1:1s, delivering performance feedback, and taking accountability for a team's delivery and wellbeing. - Clear communicator who can translate technical complexity for product and leadership stakeholders. NICE TO HAVE (BUT NOT ESSENTIAL) None of these are required. Several map to specific teams, so strength in one or more helps us match you to the right one: - Fluency with AI-assisted development, and interest in scaling agent-assisted workflows across the team (agentic CLI tools, MCP, skills, APIs) to amplify delivery. - Familiarity with confidential computing technologies: TEEs, AMD SEV, Intel TDX, or Confidential Containers (CoCo). - Experience building SaaS or PaaS layers on top of an IaaS platform. - Familiarity with RDMA, InfiniBand, or RoCE networking in GPU or HPC clusters. - Experience working distributed across time zones with counterparts in other regions. - Exposure to serverless or inference serving infrastructure.