Yash Zambre
Systems, Data & Hardware Engineer Python Linux Cloud Google (Via Tesseract) @Tezerakt
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WORK HISTORY
Systems, Data & Hardware Engineer Python Linux Cloud Google (Via Tesseract) @Tezerakt
Sunnyvale, CA, US
Built Python-based automation tools for thermal chamber control and CDU (AURAS) systems, enabling remote operation and significantly reducing manual lab intervention.Designed and deployed real-time dashboards using internal PLX tools and Google Apps Script to visualize temperature, voltage, setpoints, and system health with automated hourly updates.Developed custom CLI tools and automation scripts to support NVIDIA HGX B200 SXM6 ML accelerator bring-up, thermal validation, and test orchestration workflows.Led OVSS and RRI qualification workflows across multiple hosts, improving daily validation reliability and ensuring compliance with Google data center deployment standards.Automated network configuration and validation pipelines for Arista switches and firewalls, supporting high-speed ML system interconnects.Performed Linux system reimaging, debugging, and compatibility validation for large server fleets in R&D lab environments.Executed end-to-end electrical, functional, performance, and reliability validation of GPU and TPU platforms, including firmware upgrades, power sequencing, and system bring-up.Conducted failure analysis and root cause debugging, collaborating with hardware, software, thermal, network, and reliability teams to drive corrective actions.Owned lab resource planning, inventory management, and test environment coordination across multi-rack ML clusters.
EDUCATION
Texas A&M University
Master of Science - MS
Nanyang Technological University Singapore
exchange program, Computer Science
Vishwakarma Institute Of Technology
Bachelor's degree, Electronics and Telecommunications
ABOUT YASH ZAMBRE
I’m a Systems, Data & Hardware Engineer Engineer currently working onsite at Google (via Tesseract), where I focus on validating and automating large-scale ML hardware platforms in R&D lab environments.My work spans end-to-end system validation, Python-based automation, and infrastructure tooling for GPU and TPU platforms. I design automation pipelines to control thermal chambers and CDU systems, build dashboards for real-time telemetry, and support system bring-up, Linux debugging, and network automation for high-performance ML clusters. I work closely with hardware, software, thermal, network, and reliability teams to improve validation throughput, reliability, and operational efficiency.I hold a Master’s degree in Electrical Engineering from Texas A&M University, where I specialized in applied machine learning and computer vision. My graduate research focused on deep learning (STN-YOLO), GPU-accelerated training, and building ML applications for real-time inference, with work published at ICMLA 2024.I enjoy roles that sit at the intersection of hardware validation, software automation, and data-driven systems, and I’m particularly interested in opportunities involving automation engineering, systems/infra engineering, data engineering, and ML-ops-oriented work.Core skills: Python, Linux, Automation, Cloud (AWS/GCP), Hardware Validation, Networking, Dashboards, System Debugging, Machine Learning.
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