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Volta is a vertically integrated AI infrastructure platform building compute utilities as reliable as electricity. The company operates large-scale GPU infrastructure for AI workloads on a Kubernetes-native platform spanning multiple regions, delivering VMs, storage, and networking through custom Kubernetes operators.
As a Platform Engineer, you'll work at the intersection of infrastructure and software development, translating product requirements, operational learnings, and security guidance into durable platform capabilities. You'll design and implement Kubernetes operators and controllers managing platform resource lifecycles, collaborate with product and bring-up teams to identify pain points and build scalable solutions, and integrate security controls at the platform layer.
Key responsibilities include: designing production platform code (not configuration), treating observability as a platform concern with meaningful metrics and tooling, owning services in production with on-call participation, maintaining clean interfaces and disciplined versioning, and participating in code review and cross-team collaboration in an Agile environment.
Depending on your team, you may specialize in control plane and APIs, compute lifecycle management, storage provisioning and reliability, confidential computing capabilities, or customer-facing platform services.
You bring 3–5 years of software engineering experience with meaningful infrastructure or platform systems work. Strong backend/systems programming in production environments is essential, with proficiency in Python, Go, or Rust (or willingness to learn from C++, C#, Java backgrounds). You understand Kubernetes internals deeply: control loops, CRDs, controllers, operators, and reliable reconciliation. You're comfortable near the infrastructure layer with Linux, networking fundamentals, and distributed systems knowledge. You've designed and versioned production APIs that other teams depend on, with disciplined breaking-change handling. You operate what you build—debugging production systems and participating in incident response. Strong fundamentals in clean code, testing, version control, code review, and CI/CD round out the profile.
Nice-to-haves include fluency with AI-assisted development tools, depth in Go or Rust, confidential computing experience (TEEs, AMD SEV, Intel TDX, CoCo), security integration background, high-performance networking knowledge, distributed storage systems experience, Kubernetes operator framework familiarity, and observability tooling expertise.
