Staff Infrastructure Engineer - Models

Our AI Software Infrastructure team builds the Kubernetes-native applications, services, and platform tooling that power large-scale AI workloads across internal and customer-facing environments. In this role, you will design and operate the systems that make complex inference, training, CI/CD, and development workflows easier to deploy, scale, monitor, and support in production. If you enjoy building reliable backend and platform software, working close to infrastructure and automation, and helping raise the operational maturity of high-performance systems, this is where it all comes together.

This role is hybrid, based out of Belgrade, Serbia.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• Strong backend, infrastructure, or platform engineer with deep experience designing and running production workloads on Kubernetes.
• Strong understanding of Kubernetes-native application design, workload orchestration, scaling, reliability and production debugging.
• Experience building platform services, APIs, automation, operators, or controllers using Go or Python.
• Collaborative and adaptable, able to work across engineering, infrastructure, SRE and deployment teams.
• Experience with AI, ML, HPC, training, or inference workloads is a strong plus.

What We Need

• Design, build and operate Kubernetes-native applications, services and workloads for large-scale AI infrastructure.
• Develop operators, controllers, APIs and automation that make complex workloads easier to deploy, scale, monitor and operate.
• Define workload patterns for inference, training, CI/CD, internal development workflows and platform services.
• Improve reliability, observability and operational maturity of applications running on Kubernetes.
• Partner with SRE, infrastructure, deployment and engineering teams to support internal and customer-facing environments.

What You Will Learn

• How large-scale AI workloads are designed, deployed and operated on custom accelerator hardware.
• How inference, training, CI/CD and platform workloads behave at scale.
• How to build applications and platform services that run reliably across different Kubernetes environments.
• How internal infrastructure platforms evolve into production-grade systems used by engineering teams and customers.
• How to influence platform direction, define best practices and raise the Kubernetes maturity of the broader engineering organization.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.