Site Reliability Engineer
FuriosaAI
Site Reliability Engineer
FuriosaAI
Power-efficient AI inference accelerator chips
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.
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.
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.
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.
The defining bottleneck of the AI era is building the right hardware and software stack to run it at global scale. Furiosa is solving this challenge holistically from the ground up.
With our flagship chip, RNGD, in mass production today and our next-generation platform in development with Broadcom, we are proving that full-stack, tensor-native compute is the future of AI infrastructure. This is a pivotal moment to join our team, right as we accelerate our global expansion.
At Furiosa, you will:
Solve AI’s Most Urgent Challenge. Help build the high-performance, energy-efficient inference hardware and software required to fulfill the promise of advanced AI.
Pioneer Full-Stack Co-Design. Work with teams that are architecting solutions from silicon up through the compiler (featuring innovations like Tensor Contraction Language and Virtual ISA) and serving frameworks.
Ship Real-World Silicon, Software, and Solutions. Turn breakthrough technology into commercial deployment. RNGD is in mass production with TSMC and running live enterprise workloads for global leaders like LG AI Research and Samsung SDS.
Partner With the Industry's Best. Collaborate across an elite global ecosystem that includes TSMC, Broadcom, SK Hynix, and GUC.
Do Your Life’s Best Work. Join a brilliant, low-ego, mission-driven team in a high-trust environment that values autonomy, intellectual curiosity, and shared ambition.
FuriosaAI develops AI accelerators for data centers. Its flagship RNGD chip is a power-efficient accelerator designed to boost performance per watt for running large language models and other demanding AI workloads. The hardware is configurable with multiple processing elements to fit different workloads, and it is paired with advanced compiler technology and profiling tools to identify bottlenecks and optimize efficiency. This combination of flexible hardware and software optimization differentiates FuriosaAI from competitors, focusing on maximizing performance while keeping energy use low. The company targets data centers and enterprises that need high-performance, energy-efficient AI solutions, and aims to make AI computing more sustainable and scalable by selling its chips and related software tools and pursuing long-term contracts with large-scale customers.
Explore more opportunities at FuriosaAI