System Software Engineer

Core Responsibilities

• Implement and maintain Kubernetes workloads and supporting resources, including manifests, Helm charts, controllers, and configuration for networking, ingress, and storage, following established platform patterns.
• Deploy and operate model-serving workloads on GPU and accelerator node pools, including configuring autoscaling policies, resource requests and limits, and tenant-specific deployment configurations.
• Support model training and simulation workloads on distributed GPU systems.
• Build and maintain instrumentation on Prometheus, Grafana, and OpenTelemetry, including authoring dashboards, alerting rules, and trace and metric instrumentation for new services.
• Implement and improve CI/CD pipelines, including build, test, and deployment automation, and contribute to progressive delivery practices already in use on the platform.
• Develop and maintain infrastructure-as-code modules and automation scripts in support of repeatable, auditable infrastructure changes across cloud environments.
• Support response to production incidents, execute documented runbooks, and contribute to postmortems and follow-up remediation work.
• Investigate and resolve issues across the stack, including container, node, network, and accelerator-level problems, escalating appropriately when scope exceeds the role.
• Write clear documentation, including runbooks, internal references, and design notes for the changes you ship.
• Participate in code and design reviews, both as author and reviewer, and incorporate feedback from senior engineers into your work.

Required Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or Information Technology, plus three (3) years relevant work experience or equivalent combination of education and relevant experience
• Professional experience in cloud infrastructure, DevOps, site reliability, or backend engineering roles involving production system operation
• Working knowledge of Kubernetes in a production context, including writing and debugging manifests, understanding core resource types, and operating production workloads
• Hands-on experience with at least one major cloud provider (e.g. AWS, GCP, or OpenStack), including its compute, networking, and identity primitives
• Experience instrumenting services and consuming observability data, including writing Prometheus queries, building Grafana dashboards, or working with distributed traces
• Familiarity with CI/CD systems and the basic mechanics of automated build, test, and deployment pipelines
• Experience in configuration management and infrastructure as code tools (e.g. Ansible, Puppet, and Helm)
• Proficiency in at least one programming or scripting language used for infrastructure work (Python, Go, Rust, or Bash)
• Comfort working in a Linux environment and with standard developer tooling, including Git-based workflows

Preferred Qualifications

• Exposure to GPU or accelerator workloads in any production or research context
• Experience working in a multi-cloud environment, or strong interest in developing it
• Exposure to model-serving runtimes such as vLLM, or NVIDIA Triton
• Hands-on experience compiling, packaging, and configuring Linux software for integration into a target system
• Experience with HPC batch schedulers and MPI based workloads