Reliability and Test Engineering Manager

Position Summary: Reliability and Test Engineering Manager

Location: Valley Green, PA

Amphenol High Speed Products Group is the market leader for high speed, high bandwidth electrical connectors for the Telecom/Datacom market (Mobile Networks, Storage, Servers, Routers, Switches, etc.). Our products help to enable the electronics revolution and remain a key enabler for all the major Tier 1 OEMs globally. We are currently seeking an experienced Reliability and Test Engineering Manager to join our Product Development team. The position will be located in Valley Green, PA.

RESPONSIBILITIES:

As the Reliability and Test Engineering Manager you will provide the lead in supporting the development and validation of new products through testing and analysis of data and designs.

Reliability Strategy & System Definition

• Define and own reliability requirements and strategies for cable assemblies used in AI hardware systems, including rack-level and system-level interconnects.
• Establish mission profiles covering temperature, airflow, vibration, handling, insertion/removal cycles, bend radius, and field service conditions.
• Translate AI infrastructure requirements into quantified reliability targets (life, confidence, environmental margins, electrical performance over time).

Design-for-Reliability (Cable Assemblies)

• Partner with cable and connector design teams to influence:
  • High-speed signal integrity over life (insertion loss, return loss, crosstalk, skew, BER degradation)
  • Mechanical robustness (strain relief, jacket materials, crimp integrity, retention features)
  • Connector interfaces (fretting corrosion, plating wear, contact normal force, mating durability)
  • Thermal and airflow interactions in dense AI racks
• Drive design decisions that mitigate failure mechanisms such as:
  • Micro-motion-induced intermittents
  • Conductor and shield fatigue
  • Connector contact wear and oxidation.
  • Jacket cracking, creep, or abrasion
• Review designs for compliance with derating, material compatibility, and manufacturability best practices.

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
• Define electrical performance monitoring during stress, including:
  • Insertion loss and impedance drift
  • Eye diagrams and BER under stress
  • Intermittent detection during vibration and thermal cycling
• Lead or support HALT characterization to understand design margins and dominant failure mechanisms.

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)
• Drive corrective and preventive actions (CAPA) and verify effectiveness through retesting.

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.

Manufacturing & Supplier Engagement

• Partner with manufacturing teams to define process controls, screening, and ESS strategies for cable assemblies.
• Work directly with cable and connector suppliers to:
  • Review materials, processes, and reliability data.
  • Audit manufacturing controls.
  • Resolve field and production issues.
• Assess reliability impact of ECO/ECN changes, including materials, suppliers, tooling, or process shifts.

Technical Leadership & Documentation

• Author reliability reports, qualification summaries, and launch readiness documentation suitable for executive and customer review.
• Mentor junior engineers and promote reliability best practices across cable, hardware, and systems teams.
• Clearly communicate reliability risks and tradeoffs in high-performance AI systems.

QUALIFICATIONS:

• Bachelor’s degree in Electrical Engineering, Mechanical Engineering, or related field (Master’s preferred).
• 7+ 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).
• Experience supporting high-volume manufacturing and deployment at scale.

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

What Success Looks Like

• Cable assembly reliability risks are identified early and mitigated before large-scale AI deployment.
• Electrical performance remains stable over life in dense, high-power AI environments.
• Intermittent and field-driven failures are rapidly resolved with verified fixes.
• Field return rates and system downtime attributable to interconnects are significantly reduced