re:Cycle Reverse Logistics LLC
Logistics
SystemsSupportEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Systems Support Engineer at re:Cycle Reverse Logistics LLC. Skills: System support, ML infrastructure, Test engineering. Provide frontline technical support. Troubleshoot tester, hardware, and system-level failures”
What You'll Achieve.
Ensure test infrastructure deployed at scale; Meet highest standards of reliability and efficiency
Industry & Context.
Troubleshooting; Root cause analysis; System-level challenges
24/7 production environment
What They're Looking For.
Must Have
2+ years Linux systems administration, 1+ years Python, Java, Perl, PHP, Ruby or Bash/Shell, Bachelor's degree in Computer Science or technical degree, Knowledge of networking fundamentals, Experience in 24/7 production environment
Nice to Have
2+ years SRE, systems engineering, systems administration, DevOps, security administration, or network administration, 1+ years building scripts, tooling, and automation, Knowledge of configuration management systems, Experience in network capture and systems troubleshooting
What You'll Do.
Provide frontline technical support
and system-level failures
Own daily ticket queue management
Prioritize operationally critical issues
Perform deep technical troubleshooting
Identify root causes across hardware
and restore test equipment
Maintain operational readiness
Partner closely with Operations
Drive issue resolution
Improve overall test process stability
Execute structured root cause analysis
Implement corrective actions
Prevent recurring equipment or process failures
Support system bring-up
Support tester validation
Support configuration activities
Develop and maintain troubleshooting documentation
Develop standard work
Improve team efficiency
Simplify support workflows
Improve response times
Enhance operational effectiveness
Monitor tester health
Monitor system performance
Monitor operational trends
Proactively identify risks
Escalate issues before business impact
How You'll Work.
Team & Collaboration
Cross-functional teams; Hardware designers; Software engineers; Operations specialists
Full Job Description
Shape the future of AI infrastructure! Join RRL and develop system-level test solutions for our innovative ML acceleration hardware, deployed across our global server fleet. We're seeking highly skilled and motivated Product Test Engineers to join our RRL test & repair operation. In this role, you'll be at the forefront of validating and ensuring that test infrastructure is deployed and operating at required scale. You'll be responsible for designing and implementing comprehensive system-level test strategies that cover the full spectrum of our ML acceleration products, from individual components to fully integrated systems. This position requires a unique blend of hardware knowledge, software expertise, and systems thinking, as you'll be working at the intersection of custom silicon, complex firmware, and high-performance ML workloads. You'll collaborate closely with cross-functional teams including hardware designers, software engineers, and operations specialists to develop robust test solutions that can scale to meet the demands of RRL's global infrastructure. Your work will be crucial in identifying and resolving integration issues, optimizing system performance, and ultimately ensuring that our ML acceleration products meet the highest standards of reliability and efficiency in real-world data center environments. If you're passionate about pushing the boundaries of ML hardware testing and have a knack for solving complex system-level challenges, we want you on our team. Key job responsibilities Provide frontline technical support to manufacturing and operations teams by troubleshooting complex tester, hardware, and system-level failures impacting production throughput Own daily ticket queue management, prioritizing operationally critical issues to ensure timely resolution and minimal business disruption Perform deep technical troubleshooting of production test systems, identifying root causes across hardware, software, networking, and infrastructure dependenc
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