דרושים » מדעים מדוייקים » AI software engineer - הכשרה והשמה למשרות פיתוח

Required SOC Quality and Reliability Engineer, Cloud
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Tel Aviv, Israel; Haifa, Israel.
About the job
In this role, youll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
Our data centers are the most advanced in the world. In this role, you will help build the SoCs that power these data centers by driving quality and reliability processes from the integrated circuit perspective. You will create silicon and follow it into the field (and back) to drive improvements for the next generations of chips.
You will have an understanding of IC flows, wafer processing, testing, qualification, yield, reliability, and failure analysis. You will work with various cross functional teams to develop quality and reliability specifications, develop and deploy design guidelines, and develop and execute and test plans. Within the larger organization you will collaborate with global hardware quality and reliability teams, silicon design, validation and engineering teams.The AI and Infrastructure team is redefining whats possible. We empower our customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity.
Responsibilities
Drive the strategic definition and development of design-for-reliability (DfR) guidelines, collaborating with cross-functional subject matter experts to integrate reliability into early design stages.
Define and lead the development of qualification hardware and test methodologies, managing internal teams and external vendors to ensure silicon and package verification.
Execute comprehensive silicon and package qualification programs (including high-temperature operating life (HTOL), early life failure rate (ELFR), electrostatic discharge/latch-up (ESD/LU), biased highly accelerated stress test (b/HAST), etc.) and conduct failure analysis to resolve quality issues.
Extract and analyze data from qualification programs, high-volume manufacturing, and field returns to identify failure mechanisms and trends for yield and reliability optimization.
Develop and implement physics-based statistical Quality and Reliability models (e.g., ELF, time-dependent dielectric breakdown (TDDB), negative bias temperature instability (NBTI) to predict device failure mechanisms and lifetime behaviors.

As an Algorithm and Applied AI Scientist (DSP), you will serve as the technical lead for an innovative project focused on advanced signal processing and data analytics applications. You will work with unique datasets from various signal-generating sources, researching and developing novel models, and analysing, maturing, and extracting features that have a significant impact on industrial manufacturers globally. In this role, you will be instrumental in defining project objectives, identifying customer value, and pushing the team toward successful outcomes. Joining our AI team offers ongoing professional growth in leadership, algorithm development, research, innovations, and technical data analysis.

A Day In Your Life
Be the technical lead of an innovative project.
Develop a comprehensive understanding of industrial data by exploring its physical properties and the relationships among different data streams.
Oversee the full algorithm development cycle, spanning from initial problem definition to the deployment of production-ready feature-based AI models, SDLC starting from feature discovery that is being tracked by sensor behavior patterns.
Architect and build diverse predictive and anomaly classification models using sensor time-series and textual information.
You will gain a deep proficiency in the industrial world and the connection between datasets to generate actionable insights.
Utilize classic and cutting-edge technologies, such as Generative AI, transformers, TSFM, TGNN, deep learning, and traditional statistical methods, time-series, and textual data to generate actionable insights.
Partner with the product team and external customers to create data-driven solutions that address real-world business needs.

As an Algorithm and Applied AI Scientist (DSP), you will serve as the technical lead for an innovative project focused on advanced signal processing and data analytics applications. You will work with unique datasets from various signal-generating sources, researching and developing novel models, and analysing, maturing, and extracting features that have a significant impact on industrial manufacturers globally. In this role, you will be instrumental in defining project objectives, identifying customer value, and pushing the team toward successful outcomes. Joining our AI team offers ongoing professional growth in leadership, algorithm development, research, innovations, and technical data analysis.

A Day In Your Life:
Be the technical lead of an innovative project.
Develop a comprehensive understanding of industrial data by exploring its physical properties and the relationships among different data streams.
Oversee the full algorithm development cycle, spanning from initial problem definition to the deployment of production-ready feature-based AI models, SDLC starting from feature discovery that is being tracked by sensor behavior patterns.
Architect and build diverse predictive and anomaly classification models using sensor time-series and textual information.
You will gain a deep proficiency in the industrial world and the connection between datasets to generate actionable insights.
Utilize classic and cutting-edge technologies, such as Generative AI, transformers, TSFM, TGNN, deep learning, and traditional statistical methods, time-series, and textual data to generate actionable insights.
Partner with the product team and external customers to create data-driven solutions that address real-world business needs.

As our AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team, you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.

A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

Required SOC Quality and Reliability Engineer, Cloud
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Tel Aviv, Israel; Haifa, Israel.
About the job
In this role, youll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive TPU (Tensor Processing Unit) technology that powers our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
Our data centers are the most advanced in the world. In this role, you will help build the state-of-the-art SoCs that power these data centers by driving quality and reliability processes from the Integrated Circuit perspective. You will have an opportunity to create silicon and follow it into the field and back to drive improvements for the next-generations of chips.
You will have an understanding of Integrated Circuit (IC) flows, wafer processing, testing, qualification, yield, reliability, and failure analysis is expected. You will work with various cross-functional teams to develop quality and reliability specifications, develop and deploy design guidelines, and develop and execute and test plans. You will collaborate with global hardware quality and reliability teams, silicon design, validation and engineering teams.The AI and Infrastructure team is redefining whats possible.
Responsibilities
Lead the strategic definition and development of Design-for-Reliability (DfR) guidelines, collaborating with cross-functional subject matter experts to integrate reliability into early design stages.
Establish and direct the development of qualification hardware and test methodologies, managing internal teams and external vendors to ensure silicon and package verification.
Execute comprehensive silicon and package qualification programs (including high-temperature operating life (HTOL), early life failure rate (ELFR), electrostatic discharge and latch-up (ESD/LU), and biased highly accelerated stress test (b/HAST)) and conduct in-depth failure analysis to resolve quality issues.
Analyze data from qualification programs, high-volume manufacturing, and field returns to identify failure mechanisms and trends for yield and reliability optimization.
Develop and implement physics-based statistical quality and reliability models (e.g., early life failure (ELF), time-dependent dielectric breakdown (TDDB), or negative bias temperature instability (NBTI)) to predict device failure mechanisms and lifetime behaviors.