Volta
Team Lead, Platform Engineering
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About The Role
Volta operates GPU cloud infrastructure across bare metal, managed cloud, and serverless inference. We are building out several platform engineering teams that together own the next generation of the Volta stack, from managed bare metal and IaaS through to higher-order platform services.
You will lead one of these teams. Its area - for example compute, networking, storage, the control plane and API layer, or confidential computing - depends on where we need leadership and where your strengths lie. You will report to the VP, Platform Engineering or to the Director, Platform Engineering, EMEA depending on your location, and work closely with the product, bring-up, and security engineering teams, translating roadmap requirements, operational learnings, and security guidance into shipped, production-grade platform capabilities.
This role requires someone equally comfortable setting technical direction, writing production code, and leading a team, coordinating regularly with the other platform engineering leads on shared architecture, roadmap, and cross-team delivery, including counterparts in other time zones.
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 team 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, 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.
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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) 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, with security awareness - trust boundaries, least privilege, and secure defaults - built into how you design them.
- Proven people management: running 1:1s, delivering performance feedback, and taking accountability for a team’s delivery and wellbeing.
- A clear communicator who can translate technical complexity for product and leadership stakeholders.
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Nice to Have
- 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.
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