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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: 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. Key Responsibilities 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. Minimum Qualifications 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. Preferred Qualifications 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. Contact recruit@furiosa.ai
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.