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 global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.
The Role:
The Reliability Testing Engineer will be responsible for developing, executing, and analyzing reliability test programs to ensure the long-term performance and robustness of complex electronic and electromechanical systems. This role involves hands-on testing across thermal, mechanical, and electrical domains, as well as statistical analysis to characterize product lifetime and identify potential failure mechanisms.
The engineer will design test setups, define stress profiles, manage instrumentation and data collection, and collaborate with cross-functional teams to translate reliability results into actionable design and process improvements. This position requires close interaction with Reliability, Failure Analysis, Design, Quality, and Manufacturing teams.
Responsibilities:
- Develop and execute reliability test plans such as Accelerated Life Testing (ALT), Environmental Stress Screening (ESS), and Highly Accelerated Stress Testing (HAST).
• Design and implement test fixtures, instrumentation setups, and monitoring systems for mechanical, thermal, and electrical reliability experiments.
• Establish test conditions, durations, and sample sizes using physics-of-failure and statistical reliability modeling.
• Perform environmental testing (temperature cycling, humidity, vibration, and pressure) to simulate real-world stresses.
• Configure and monitor data acquisition systems (pressure, temperature, current, flow, etc.) to ensure high-quality test data.
• Conduct failure correlation and root cause analysis in partnership with the Failure Analysis team.
• Apply Weibull and Arrhenius modeling to analyze failure distributions and estimate lifetime metrics (MTTF, MTBF).
• Support design-of-experiment (DOE) studies to validate material, assembly, or design changes for reliability impact.
• Coordinate with vendors and internal teams to ensure test equipment calibration, readiness, and repeatability.
• Document all results, prepare detailed reliability test reports, and present findings to stakeholders.
Skills & Qualifications:
- BS or MS in Mechanical, Electrical, or Reliability Engineering, or equivalent technical discipline.
• 5+ years of experience in reliability or environmental testing for electronic or electromechanical products.
• Strong understanding of reliability standards and methods (JEDEC, IPC, MIL-STD, ASTM, AEC-Q100).
• Hands-on experience in thermal cycling, vibration, humidity, or mechanical fatigue testing.
• Familiarity with data acquisition tools and test automation (LabVIEW, Python, PLCs, or equivalent).
• Experience performing or interpreting statistical reliability analysis (Weibull, lognormal, MTTF/MTBF).
• Knowledge of GD&T, metrology, and mechanical inspection tools such as calipers, micrometers, and CMMs.
• Exposure to cross-sectioning, microscopy, and precision measurement for failure validation is a plus.
• Strong communication and documentation skills with the ability to summarize complex findings clearly.
• Highly organized, detail-oriented, and comfortable working in a fast-paced, lab-based environment.
The base salary range for this position is $180,000 to $250,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.
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 2025.
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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Top Skills
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.
The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.
Join us: https://cerebras.net/careers/
