
Leading supplier of end-to-end high speed Ethernet and InfiniBand intelligent interconnect solutions and services.

Leading supplier of end-to-end high speed Ethernet and InfiniBand intelligent interconnect solutions and services.
Company: NVIDIA
Core focus: Accelerated computing and AI infrastructure (GPUs, data center and AI systems, edge/robotics, automotive)
Founded: April 1993
Public company / Investor relations: Maintains an active investor relations portal with quarterly results and presentations
High-performance graphics, machine learning/AI acceleration, and full-stack AI infrastructure for enterprise, cloud and edge use cases.
1993
Semiconductors / AI infrastructure
Historical financing and IPO-related information are documented on financial profiles; NVIDIA also deploys strategic investments in startups (examples reported)
$2B
Reported $2B investment to support CoreWeave's AI compute expansion
The Platform Reliability Engineer plays a critical role in ensuring the excellence and reliability of our Unified Commerce Platform (UCP), a full-fledged commerce platform with customer-facing components. This position bridges development and reliability assurance, requiring both strong software engineering skills and a reliability mindset. Through rigorous testing frameworks, performance monitoring, and proactive issue identification, this role directly contributes to consistently meeting and exceeding our platform SLA commitments across all tenant environments. The ideal candidate will develop robust testing frameworks, automation solutions, and reliability processes that support our platform's scale, ensuring seamless experiences across subscription management, payment processing, fraud prevention, and customer engagement functionalities. This role has a direct impact on customer trust, revenue integrity, and platform reliability. What You'll Be Doing * Design automated testing strategies and frameworks across unit, integration, API and end-to-end levels for critical commerce flows * Create performance testing frameworks to validate platform scalability, resilience, and optimization opportunities * Develop comprehensive monitoring solutions with alerting systems that track platform health and SLA compliance * Implement specialized test frameworks for security controls and compliance validation across payment and personal data * Design test data strategies with generation frameworks and synthetic data solutions that maintain security standards * Establish reliability processes, including shift-left testing approaches and incident response protocols * Build scalable automation infrastructure supporting growing platform capabilities with consistent test environments * Develop comprehensive validation strategies with risk-based approaches, prioritizing critical business functions What We Want To See * Bachelor's degree in Computer Science, Software Engineering, or related technical field * 5+ years of experience in software development, test automation, or quality engineering * Strong programming skills in languages such as Golang, Java, Python, or JavaScript * Experience designing and implementing automated test frameworks * Knowledge of testing practices in agile development environments * Experience with API testing and web service validation * Experience with testing in cloud environments (AWS, Azure, or GCP) * Background in performance testing methodologies and tools * Understanding of database testing and data validation techniques * Familiarity with security testing approaches for sensitive financial applications Ways To Stand Out From The Crowd * Master's degree in a relevant field * Experience with commerce platforms, payment systems, or subscription services, consumer-facing web application testing * Background in chaos engineering or resilience testing. Expertise in accessibility testing and inclusive design validation is a bonus. * Background in test-driven development or behavior-driven development and experience implementing quality processes in cross-functional teams * Knowledge of machine learning testing approaches for fraud detection or risk model JR1996855
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