לוח משרות דרושים בוגרי יחידות טכנולוגיות/אקדמיה בתל אביב יפו

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
As a SoC Product Engineer, you will design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You will develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing and mission-mode operation. You will work to support the machinery that goes into our data centers affecting Google users.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Clouds Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

Responsibilities
Develop and implement strategies for high volume manufacturing of SoC products, including troubleshooting, ATE test coverage optimization, DPPM reduction, Test cost reduction, power and performance assurance, and product data integration and correlation between system, ATE, and System Level Test (SLT).
Drive interactions with wafer fabs and OSATs, own and drive checkpoints for key quality metrics.
Drive volume ramp and mass production through test program releases, volume data analytics, lot disposition, extended test time reduction, yield improvement, and RMA handling.
Collaborate with cross-functional teams across the globe including ATE and SLT Test Engineering, Q&R, Packaging, Supplier Management and Operations to build, deploy, and maintain a high volume manufacturing screening solution.
Support setup and maintenance of test, diagnosis, and yield analysis infrastructure, including RMA support.

Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google Cloud's servicesboth our internally critical and our externally-visible systemshave reliability, uptime appropriate to customer's needs and a fast rate of improvement. Additionally SREs will keep an ever-watchful eye on our systems capacity and performance.

Much of our software development focuses on optimizing existing systems, building infrastructure and eliminating work through automation. On the SRE team, youll have the opportunity to manage the complex challenges of scale which are unique to Google Cloud, while using your expertise in coding, algorithms, complexity analysis and large-scale system design. SRE's culture of intellectual curiosity, problem solving and openness is key to its success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to create an environment that provides the support and mentorship needed to learn and grow.

Responsibilities
Improve the life-cycle of services from inception and design, through deployment, operation, and refinement.
Manage support services before they go live through activities such as system design consulting, developing software platforms and frameworks, capacity planning, and launch reviews.
Provide guidance to other team members on managing availability and performance of mission critical services, on building automation to prevent problem recurrence, and on building automated responses for non-exceptional service conditions.
Maintain services once they are live by measuring and monitoring availability, latency, and overall system health, and lead sustainable incident response.
Scale systems sustainably through mechanisms like automation and evolve systems by driving changes that improve reliability and velocity.