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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Reliability Engineer - **Company:** Amphenol TCS - **Location:** Raleigh, NC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Data Centers, Decision Support Systems, Reliability Engineering, Data Analytics - **Published:** June 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=59a82c6feb350a39 ## About the Role Do you have experience in Troubleshooting electrical systems?, * Bachelor's degree in Electrical Engineering, Mechanical Engineering, or related field * 5+ years of experience in reliability engineering, validation, or failure analysis for cable assemblies, connectors, or high-speed interconnects. * Strong understanding of high-speed electrical performance and how it degrades over time and stress. * Hands-on experience with mechanical and environmental testing of cable assemblies. * Proven experience diagnosing intermittent and mixed electrical-mechanical failures. * Proficiency with reliability statistics and life data analysis (Weibull, ALT correlation). * Strong cross-functional communication and technical leadership skills. Additional Qualifications * Experience with AI data center or hyperscale infrastructure. * Familiarity with high-speed copper and hybrid cable technologies (DAC, AOC, internal high-speed harnesses). * Working knowledge of SI/PI concepts (eye margin, BER, impedance control) as they relate to reliability. * Familiarity with relevant standards (IPC, IEC, Telcordia, MIL-STD, or equivalent). * Ability to travel internationally and domestically, up to 20%. Key Competencies * Systems-level thinking across electrical, mechanical, and environmental domains * Strong lab-based troubleshooting and root cause analysis * Data-driven decision making under ambiguity. * Ability to balance performance, reliability, cost, and deployment speed. * Clear, concise technical communication ## Description Test, Validation & Qualification * Develop and execute reliability and qualification test plans specific to AI-scale cable assemblies, including: + Thermal cycling and thermal aging + Temperature/humidity bias (THB) + Vibration and mechanical shock + Flex, bend, torsion, and pull testing + Connector mating/unmating durability + Accelerated life testing (ALT) with combined stresses Failure Analysis & Root Cause * Lead complex electrical-mechanical failure analysis for cable assemblies, including: + Intermittent opens and shorts + Signal degradation over life + Connector fretting, corrosion, or plating wear + Crimp, weld, or termination failures. * Apply structured root cause methods (8D, 5-Why, Fishbone) supported by: + Electrical probing and TDR + X-ray and micro sectioning + Optical and SEM analysis (as applicable) Reliability Modeling & Data Analytics * Build life and reliability models (Weibull, Arrhenius, Coffin-Manson, Miner's rule) appropriate for cable and connector failure mechanisms. * Correlate accelerated test results with field data to validate models and confidence levels. * Analyze FRACAS, RMA, and deployment data to identify systemic risks across large-scale AI infrastructure. * Understanding of FIT and MTBF software Technical Leadership & Documentation * Author reliability reports, qualification summaries, and launch readiness documentation suitable for executive and customer review. * Clearly communicate reliability risks and tradeoffs in high-performance AI systems. ## Related Videos - [Leading with Reliability: Applying SRE Principles to Build Stronger Engineering Organizations](https://www.wearedevelopers.com/videos/100185-leading-with-reliability-applying-sre-principles-to-build-stronger-engineering-organizations) - [The Sustainability Race: AI's Promises, Pitfalls and Potential](https://www.wearedevelopers.com/videos/100155-the-sustainability-race-ai-s-promises-pitfalls-and-potential) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [How Data is Shaping our Games](https://www.wearedevelopers.com/videos/176-how-data-is-shaping-our-games) ## Related Articles - [From developer to manager – what does it take to become an engineering manager?](https://www.wearedevelopers.com/magazine/42-from-developer-to-manager-what-does-it-take-to-become-an-engineering-manager) - [Top Characteristics of a Software Engineer](https://www.wearedevelopers.com/magazine/166-top-characteristics-of-a-software-engineer) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Why Attend a Developer Event in 2026?](https://www.wearedevelopers.com/magazine/688-why-attend-a-developer-event-in-2026) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)