Cerebras Systems
Artificial Intelligence • Hardware • Healthcare Insurance
Cerebras Systems is a pioneering company that focuses on developing advanced AI hardware, specifically the Cerebras Wafer Scale Engine, which delivers unparalleled performance in AI inference, outperforming traditional GPU setups. Their cutting-edge technology enables organizations like Mayo Clinic and AlphaSense to run state-of-the-art AI models with remarkable speed and efficiency. With flexible deployment options including cloud and on-premises solutions, Cerebras is transforming the landscape of AI capabilities for innovative teams across various industries.
Principal Engineer, AI Inference Reliability
Job not on LinkedIn
October 30
Cerebras Systems
Artificial Intelligence • Hardware • Healthcare Insurance
Cerebras Systems is a pioneering company that focuses on developing advanced AI hardware, specifically the Cerebras Wafer Scale Engine, which delivers unparalleled performance in AI inference, outperforming traditional GPU setups. Their cutting-edge technology enables organizations like Mayo Clinic and AlphaSense to run state-of-the-art AI models with remarkable speed and efficiency. With flexible deployment options including cloud and on-premises solutions, Cerebras is transforming the landscape of AI capabilities for innovative teams across various industries.
📋 Description
• Define and drive reliability strategy: establish SLOs and ensure alignment across engineering. • Design and implement reliability mechanisms: build and evolve systems for fault detection, graceful degradation, failover, throttling, and recovery across multiple regions and data centers. • Lead large-scale incident management: own postmortems, root-cause analysis, and prevention loops for reliability-related incidents. • Architect for reliability and observability: influence system design for redundancy, durability, and debuggability. • Develop reliability tooling: create internal tools and frameworks for chaos testing, load simulation, and distributed fault injection. • Collaborate broadly: work across software, infrastructure, and hardware teams to ensure reliability is embedded into every layer of our inference service. • Monitor and communicate reliability metrics: build dashboards and alerts that measure service health and provide actionable insights. • Mentor and influence: guide engineers and set best practices for designing, testing, and operating reliable large-scale systems.
🎯 Requirements
• Bachelor's or master's degree in computer science or related field. • 7+ years of experience in backend, infrastructure, or reliability engineering for large-scale distributed systems. • Strong programming skills in at least one popular backend programming language such as Python, C++, Go, or Rust. • Deep and hard-earned experience of reliability principles: SLO/SLI/SLA design, incident response, and postmortem culture. • Excellent communication and cross-functional leadership skills. • Bonus: prior experience building large-scale AI infrastructure systems.
🏖️ Benefits
• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Professional development opportunities
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