לוח משרות דרושים

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 Design Verification Engineer, you will work as part of a Research and Development team building verification components, constrained-random testing, system testing, and verification closure. As part of our server chip design team, you will verify digital designs. You will collaborate with design and verification engineers in active projects and perform verification. You will be responsible for the full life-cycle of verification which can range from verification planning, test execution or collecting and closing coverage.

The AI and Infrastructure team is redefining whats possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Responsibilities
Plan the verification of digital design blocks by fully understanding the design specification and interacting with design engineers to identify important verification scenarios.
Create and enhance constrained-random verification environments using SystemVerilog or formally verify designs with SystemVerilog Assertions (SVA) and industry leading formal tools.
Identify and write all types of coverage measures for stimulus and corner-cases.
Debug tests with design engineers to deliver functionally correct design blocks.
Apply close coverage measures to identify verification holes and to show progress towards tape-out.

Technical Leadership & Architecture: Drive data infrastructure strategy and establish standardized patterns for AI/ML workloads, with direct influence on architectural decisions across data and engineering teams
DataOps Excellence: Create seamless developer experience through self-service capabilities while significantly improving data engineer productivity and pipeline reliability metrics
Cross-Functional Innovation: Lead collaboration between DevOps, Data Engineering, and ML Operations teams to unify our approach to infrastructure as code and orchestration platforms
Technology Breadth & Growth: Work across the full DataOps spectrum from pipeline orchestration to AI/ML infrastructure, with clear advancement opportunities as a senior infrastructure engineer
Strategic Business Impact: Build scalable analytics capabilities that provide direct line of sight between your infrastructure work and business outcomes through reliable, cutting-edge data solutions
What you'll be doing
Design Data-Native Cloud Solutions - Design and implement scalable data infrastructure across multiple environments using Kubernetes, orchestration platforms, and IaC to power our AI, ML, and analytics ecosystem
Define DataOps Technical Strategy - Shape the technical vision and roadmap for our data infrastructure capabilities, aligning DevOps, Data Engineering, and ML teams around common patterns and practices
Accelerate Data Engineer Experience - Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities that empower data teams to deliver insights faster with higher reliability
Engineer Robust Data Platforms - Build and optimize infrastructure that supports diverse data workloads from real-time streaming to batch processing, ensuring performance and cost-effectiveness for critical analytics systems
Drive DataOps Excellence - Collaborate with engineering leaders across data teams, champion modern infrastructure practices, and mentor team members to elevate how we build, deploy, and operate data systems at scale