Staff Engineer
On-siteAustin, Texas, United States
Austin, Texas, United StatesOn-siteFull TimeSenior LevelAI HardwareMedium
Full TimeSenior LevelMediumAI Hardware
Job Summary
Own software engineering efforts across the full SDLC, including implementation, automated testing, integration, and production readiness for the rack management solution. Drive issues to resolution while collaborating across teams to ensure complex systems work seamlessly and reliably. Configure and test new Graphcore AI hardware and systems using Continuous Deployment and Infrastructure-as-code in internal and external datacentres. Work with Datacenter Operations Engineers to maintain and operate the fleet of AI systems at peak performance, and execute corrective actions for systems not operating correctly.
Required Qualifications
- Bachelor's degree or equivalent practical experience in a relevant subject
- Experience with RESTful API development
- Experience building, deploying, and operating containerized workloads using Kubernetes and container runtimes such as Docker or Podman
- Experience with managing production Kubernetes clusters and workloads
- Programming experience with Go
- Hands-on experience deploying and operating infrastructure using Infrastructure-as-Code, source code version control, and CI/CD automation tools (e.g. Terraform/OpenTofu, Ansible, GitLab, GitHub Actions, Git version control)
- Experience with Redfish for datacenter hardware management, telemetry, provisioning, and control
- Experience specifying, scoping, estimating and detailing work plans in an AGILE and SCRUM framework, including priorities, risks, issues, impacts and constraints
- Strong Linux systems engineering experience, including administration, automation, and scripting with Bash and Python
Desired Qualifications
- Experience with AI coding assistants (Codex, Claude, etc)
- Experience with Kubernetes operator development (Custom resources)
- Experience with High Performance Computing (HPC) environments using SLURM or similar batch workload solutions
- Experience with virtualized deployments and the technologies they rely on (e.g. Open vSwitch, KVM, QEMU)
- Experience with distributed object, block, and file storage (e.g., Ceph)
- Experience in end-to-end deployment automation and CI of containerized services. Complete automation of pipelines for build, test, deploy, manage, alert, destroy, rebuild
- Experience with solutions for monitoring and observability (e.g. Grafana, Prometheus, OpenSearch/ElasticSearch, Loki, Mimir, OpenTelemetry, Fluentd, Kafka)
- Experience with managed switch configuration (e.g. EOS, SONiC, DNOS)
- Experience with PyTorch for AI workloads
- Solid understanding of cloud and infrastructure technologies, including APIs, virtualization, networking, block storage, resource management, and monitoring systems
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