Software Engineer, Site Reliability Engineer
About FuriosaAI
FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon.
Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise.
About the Role
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:
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Reliability Architecture: Improve production systems so failures are isolated, degraded gracefully, detected quickly, and recovered safely.
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Observability & SLOs: Build the metrics, logs, traces, dashboards, alerts, and service-level indicators required to understand user-facing reliability.
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Production Engineering: Reduce operational toil through automation, self-service workflows, safer rollouts, and hands-on engineering contributions.
Key Responsibilities
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Define and evolve reliability goals for production systems through SLIs, SLOs, error budgets, and meaningful operational metrics.
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Design and build observability foundations that make system behavior, user impact, performance bottlenecks, and failure modes measurable and actionable.
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Analyze production systems end-to-end, identify reliability risks across software, infrastructure, and networking boundaries, and drive architectural improvements.
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Improve change safety and failure recovery through better rollout strategies, capacity planning, load validation, graceful degradation, and incident learning loops.
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Reduce operational toil by building automation, internal tooling, and self-service workflows that make production systems easier to operate and harder to misuse.
Minimum Qualifications
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Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
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Strong programming skills in one or more general-purpose languages such as Rust, Python, , or Go.
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Solid understanding of operating systems, computer networks, and cloud-native or container-based environments.
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Ability to analyze technical problems and communicate clearly with engineering teams.
Preferred Qualifications
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Experience improving reliability of production systems using SLOs, observability, incident analysis, rollout safety, and error-budget-driven decision making.
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Experience designing or operating distributed systems where failures, overload, latency, and capacity limits must be explicitly managed.
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Experience building automation, internal tooling, or self-service workflows that reduce operational toil and improve engineering productivity.
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Experience working across software, infrastructure, networking, and security boundaries to diagnose problems and drive architectural improvements.