Principal Cloud Platform Engineer, SambaNova Systems

• B.S. in Computer Science, Computer Engineering, or related field
• 3+ years of experience in a Site Reliability Engineering, DevOps
• Experience supporting a large-scale, customer-facing service in a public cloud environment (AWS, GCP, Azure)
• Strong programming and scripting skills in languages like Python, Go, Rust, or Java
• Proven experience with containerization and orchestration technologies (Docker and Kubernetes)
• Deep understanding of monitoring and observability principles and tools (e.g., Prometheus, Grafana, ELK Stack, Datadog)
• Experience with Infrastructure as Code (e.g., Terraform, CloudFormation)
• Experience with CI/CD principles and tools (e.g., Jenkins, GitHub Actions, ArgoCD)
• Strong Linux/Unix system administration fundamentals

Desirable

• The Cloud Platform team owns the production inferencing service that serves SambaNova's models to customers on RDU accelerators, including capacity planning, deployment, monitoring, and incident response across regions in the United States, Asia, Europe, and Latin America
• As a Principal Cloud Platform Engineer, you will be specializing in our AI Inferencing Service and will be the guardian of its reliability, performance, and scalability
• You will bridge the gap between software development and operations, applying an engineering mindset to solve operational challenges
• Your primary focus will be ensuring our inference endpoints have exceptional uptime, low-latency response times, and efficient resource utilization, directly impacting the experience of our customers and the success of our AI products
• This role includes participating in a shared on-call rotation to maintain 24/7 service reliability
• Shared ownership of the production inferencing service across regions, covering availability, latency, performance, change management, and capacity planning
• Standing-up and automating AI infrastructure in new regions
• Participating in a shared primary/secondary on-call rotation, and leading incident response
• Building monitoring, alerting, and dashboards in Prometheus, Grafana, and Datadog for service health, model latency and throughput, and accelerator utilization
• Finding and eliminating performance bottlenecks
• Designing auto-scaling policies that handle variable inference loads
• Managing cloud and on-prem infrastructure as code in Terraform and Ansible
• Building CI/CD pipelines that safely deploy new model versions and service updates
• Forecasting infrastructure needs against the product roadmap and usage trends, and working with finance to manage cloud spend
• Defining and reporting on SLOs and SLIs for the inferencing platform, using that data to prioritize reliability work