Furiosa Ai
Software Engineer, Site Reliability Engineer
1mo ago
WorldwideRemotekubernetescloudnetworkingobservabilitydeployment pipelinesapi
Apply software engineering to improve reliability, scalability, security, and operability of production infrastructure and customer-facing services.
Responsibilities
- As a Site Reliability Engineer, you will apply software engineering to improve the reliability, scalability, security, and operability of FuriosaAI’s production infrastructure and customer-facing services. You will work across baremetal Kubernetes clusters, cloud control planes, networking, observability systems, deployment pipelines, and API services running on Furiosa NPUs.
- We are looking for an engineer who can reason about production systems end-to-end, identify reliability risks across service and infrastructure boundaries, build the observability foundation required to understand them, and drive improvements through code, configuration, automation, and architectural changes.
- In this role, your mission is defined by three primary pillars:
- Reliability Architecture: Improve production systems so failures are isolated, degraded gracefully, detected quickly, and recovered safely.
- Observability & SLOs: Build the metrics, logs, traces, dashboards, alerts, and service-level indicators required to understand user-facing reliability.
- Production Engineering: Reduce operational toil through automation, self-service workflows, safer rollouts, and hands-on engineering contributions.
- Define and evolve reliability goals for production systems through SLIs, SLOs, error budgets, and meaningful operational metrics.
- Design and build observability foundations that make system behavior, user impact, performance bottlenecks, and failure modes measurable and actionable.
- Analyze production systems end-to-end, identify reliability risks across software, infrastructure, and networking boundaries, and drive architectural improvements.
- Improve change safety and failure recovery through better rollout strategies, capacity planning, load validation, graceful degradation, and incident learning loops.
- Reduce operational toil by building automation, internal tooling, and self-service workflows that make production systems easier to operate and harder to misuse.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Strong programming skills in one or more general-purpose languages such as Rust, Python, , or Go.
- Solid understanding of operating systems, computer networks, and cloud-native or container-based environments.
- Ability to analyze technical problems and communicate clearly with engineering teams.
Nice to have
- Experience improving reliability of production systems using SLOs, observability, incident analysis, rollout safety, and error-budget-driven decision making.
- Experience designing or operating distributed systems where failures, overload, latency, and capacity limits must be explicitly managed.
- Experience building automation, internal tooling, or self-service workflows that reduce operational toil and improve engineering productivity.
- Experience working across software, infrastructure, networking, and security boundaries to diagnose problems and drive architectural improvements.