Silicon Validation Engineer

Summary

Apple creates consumer technology products and experiences through collaborative innovation. The Silicon Validation Engineer develops and maintains software for lab characterization, validation, and debugging of analog and mixed-signal circuits and silicon subsystems, while building embedded systems, data pipelines, AI and machine learning solutions, computer vision tools, and parallel computing workflows for silicon validation.

Responsibilities

• Develop, maintain, and optimize software for lab characterization, validation, and debug of analog and mixed-signal embedded circuits and silicon subsystems
• Design and develop embedded system software for lab instrumentation and test platforms, including low-level hardware control, peripheral communication interfaces (SPI, I2C, UART), and hardware bring-up support for analog and mixed-signal test environments
• Perform signal analysis and processing, including time-domain and frequency-domain analysis of analog and mixed-signal waveforms, to extract electrical parameters and support circuit characterization and validation
• Design and implement data warehousing, visualization, and post-processing pipelines to manage and analyze large volumes of measurement data from lab bring-up and silicon characterization workflows
• Develop artificial intelligence and machine learning solutions, including deep learning models and convolutional neural networks (CNNs), for post-silicon data analysis, predictive modeling, pattern recognition, and intelligent test and search algorithms
• Develop and deploy computer vision pipelines for visual recognition, including automated monitoring of lab bench and equipment status and automated chip handling
• Leverage parallel computing frameworks, including MPI, OpenMP, and CUDA, to accelerate compute-intensive signal processing, simulation, and machine learning workloads across multi-core CPU and GPU architectures
• Apply software design principles, including algorithms and data structures, to architect scalable, maintainable, and reusable software components across lab tooling and validation infrastructure
• Convert and adapt test sequences into formats compatible with lab software, and implement new software features in response to lab user requirements
• Integrate lab software with upstream design tools and downstream system software, including establishing interfaces and automated test sequence conversion, to enable end-to-end silicon validation and characterization workflows
• Provide debug support during lab bring-up and characterization, and resolve software-related issues and questions raised by lab users

Skills

• Master's degree or foreign equivalent in Electrical Engineering, Electronics Engineering, or a related field
• Designing and analyzing electronic circuits, including analog, mixed-signal, and power circuits, to support validation and characterization of silicon IPs and subsystems
• Designing embedded systems using microcontrollers or FPGAs, including experience with hardware bring-up, peripheral communication interfaces (SPI, I2C, UART), and low-level hardware control for lab instrumentation and test platforms
• Utilizing programming and scripting languages, including Python, C and Tcl, to develop, optimize, and maintain test automation scripts, measurement utilities, and lab tooling for analog and mixed-signal validation workflows
• Performing signal analysis and processing, including time-domain and frequency-domain analysis of analog and mixed-signal waveforms, using tools such as NumPy, SciPy, and MATLAB to extract electrical parameters and characterize circuit behavior
• Designing and maintaining data management systems and pipelines, including collecting, storing, querying, and visualizing large volumes of measurement data using SQL databases and data analytics tools to support silicon bring-up and tape-out decisions
• Applying artificial intelligence and deep learning techniques, including using frameworks such as PyTorch or TensorFlow, to develop predictive models and automate pattern recognition in analog and mixed-signal measurement data
• Utilizing PyTorch, TensorFlow, and OpenCV to design and train deep learning models, including convolutional neural networks (CNNs), for automating pattern recognition on analog and mixed-signal measurement data and developing computer vision pipelines for visual monitoring of lab equipment and automated chip handling

Qualifications

Must Haves

• Master's degree or foreign equivalent in Electrical Engineering, Electronics Engineering, or a related field
• Designing and analyzing electronic circuits, including analog, mixed-signal, and power circuits, to support validation and characterization of silicon IPs and subsystems
• Designing embedded systems using microcontrollers or FPGAs, including experience with hardware bring-up, peripheral communication interfaces (SPI, I2C, UART), and low-level hardware control for lab instrumentation and test platforms
• Utilizing programming and scripting languages, including Python, C and Tcl, to develop, optimize, and maintain test automation scripts, measurement utilities, and lab tooling for analog and mixed-signal validation workflows
• Performing signal analysis and processing, including time-domain and frequency-domain analysis of analog and mixed-signal waveforms, using tools such as NumPy, SciPy, and MATLAB to extract electrical parameters and characterize circuit behavior
• Designing and maintaining data management systems and pipelines, including collecting, storing, querying, and visualizing large volumes of measurement data using SQL databases and data analytics tools to support silicon bring-up and tape-out decisions
• Applying artificial intelligence and deep learning techniques, including using frameworks such as PyTorch or TensorFlow, to develop predictive models and automate pattern recognition in analog and mixed-signal measurement data
• Utilizing PyTorch, TensorFlow, and OpenCV to design and train deep learning models, including convolutional neural networks (CNNs), for automating pattern recognition on analog and mixed-signal measurement data and developing computer vision pipelines for visual monitoring of lab equipment and automated chip handling

Benefits

• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs.
• Eligibility for discretionary restricted stock unit awards.
• Ability to purchase Apple stock at a discount through Apple’s Employee Stock Purchase Plan, if voluntarily participating.
• Comprehensive medical and dental coverage.
• Retirement benefits.
• A range of discounted products and free services.
• Reimbursement for certain educational expenses, including tuition, for formal education related to advancing your career at Apple.
• This role might be eligible for discretionary bonuses or commission payments.
• This role might be eligible for relocation.