Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Role Summary
Quality, reliability, and uptime are foundational to scaling Cerebras systems. We are seeking an engineer to define and build our prognostics and health monitoring (PHM) capability—developing frameworks to monitor, assess, and predict hardware health across our fleet.
In this role, you will transform telemetry and operational data into actionable insights and automated responses, enabling early detection of degradation, accurate failure prediction, and proactive actions to keep systems highly available, performant, and resilient.
This is a highly cross-functional role spanning reliability engineering, data science, and system software, with broad influence across hardware, software, and fleet operations.
Responsibilities
Define the vision, architecture, and roadmap for PHM across deployed systems
Design and scale frameworks for health assessment, anomaly detection, and predictive failure modeling
Develop and productionize probabilistic models for failure risk, degradation, and remaining useful life
Analyze large-scale telemetry, logs, and service data to identify systemic drivers of failures and disruptions
Establish health metrics, scoring systems, and fleet-level observability to communicate system risk
Partner with system software to integrate monitoring, alerting, and automated mitigation into production
Drive closed-loop systems (detection → diagnosis → action → validation)
Influence hardware design, qualification, and operations through data-driven insights
Skills & Qualifications
Required:
Bachelor’s or Master’s in Engineering, Computer Science, Data Science, or related field
8+ years in reliability engineering, data science, fleet analytics, or similar
Strong Python and SQL for large-scale data analysis and modeling
Experience building and deploying predictive models in production
Expertise in applied statistics and probabilistic modeling (e.g., survival analysis, hazard models, Bayesian methods)
Experience with large-scale telemetry or distributed system datasets
Proven ability to define ambiguous problems and deliver scalable solutions
Preferred:
Experience with HPC systems, AI infrastructure, or datacenter environments
Background in PHM, predictive maintenance, or reliability analytics at scale
Familiarity with RUL estimation and degradation modeling
Understanding of observability systems, telemetry pipelines, and real-time monitoring
Background in hardware reliability and failure modes in complex systems
The base salary range for this position is $150,000 to $250,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
Read our blog: Five Reasons to Join Cerebras in 2026.
Apply today and become part of the forefront of groundbreaking advancements in AI!
_Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. __We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. _We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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