Senior Inference Platform SDET

Responsibilities

• Design, build, and maintain test infrastructure and automation for deploying and validating the Cerebras Inference Platform.
• Validate the platform across environments — from cloud-managed Kubernetes to deployments running on Cerebras hardware.
• Test and verify deployment infrastructure including Kubernetes workloads, CI/CD pipelines, ingress and service discovery, NGINX, and load balancing.
• Collaborate closely with the Inference Platform development team to ensure new features and platform capabilities ship reliably.
• Investigate and debug complex issues spanning networking, orchestration, deployment, and distributed services.
• Develop and maintain testbeds used to validate platform performance, scalability, and reliability.
• Identify failure points, bottlenecks, and edge cases that impact platform stability and inference performance.
• Contribute to test plans and validation strategies for new platform features and releases.
• Improve observability, diagnostics, and debugging workflows across the inference platform stack.
• Partner with engineering teams to ensure high-quality, production-ready releases of the Cerebras Inference Platform.

Minimum Skills & Qualifications

• 5+ years of experience in software engineering, QA/quality engineering, systems engineering, or infrastructure development.
• Strong programming skills in Python and/or Go (experience with both is a plus).
• Experience building automation tools, testing frameworks, or internal developer tooling.
• Hands-on experience with CI/CD systems (e.g., Jenkins).
• Experience debugging complex systems, distributed services, or networked infrastructure.
• Familiarity with systems-level development, infrastructure tooling, or platform integration.
• Strong problem-solving skills and the ability to investigate issues across multiple system and infrastructure layers.
• Excellent communication and collaboration skills.
• Experience mentoring junior engineers.

Preferred Skills

• Hands-on experience with Kubernetes and container orchestration in a real production or staging environment.
• Experience with cloud-managed Kubernetes such as Amazon EKS.
• Experience with GitOps/deployment tooling (e.g., ArgoCD).
• Familiarity with ingress controllers, service discovery, NGINX, and load balancing.
• Experience with build systems such as Bazel.
• Experience with cluster tooling and operations (e.g., k9s).
• Exposure to performance debugging, profiling, or system observability tools.
• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads.