• Plan, analyze and report the performance of different application-specific image sensors, e.g. Time-of-flight, structured light, stereo camera, etc.
• Work with AI algorithm team to propose optimal image sensor solution for various AI vision related application, e.g. 3D facial recognition.
• Benchmark different sensor interfaces (e.g. MIPI, LVDS, eDP, etc.) based on system requirements.
• Work with hardware team to integrate image sensor with embedded hardware platform (e.g. Rockchip RK33/32 series, Hi-Silicon Hi3519/3516, Qualcomm 439, NVIDIA Jetson, etc.).
• Recommend lens for image sensor and analyze overall optical performance.

• Develop and accelerate various edge AI applications in embedded hardware or GPU platform. Initial focus is AI vision related research and development.
• Implement and optimize DNN algorithm in embedded hardware or GPU platform.
• Prototype/system development and performance evaluation.
• Research, benchmark and recommend implementation platform for different edge AI applications.
• Driver and software development.

• Carry out research and development on innovative machine learning and deep neural network applications;
• Machine learning algorithm analysis, data set training and modelling;
• Perform algorithm evaluation and quality assessment;
• Optimize DNN algorithms for FPGA and software implementation;
• Support FPGA and/or IC implementation;
• Develop software modules for CPU and embedded platforms.

• Work closely with hardware engineers to develop AI (artificial intelligence) drivers and applications, including but not limited to machine vision, quality inspection, and health management, etc.;
• Implement and optimize visual intelligence algorithms in firmware;
• Develop software modules for CPU and embedded platforms;
• Participate in visual intelligence system design and implementation;
• Collaborate with the team to define the product vision, review specs, and make architecture decisions;
• Support application demos.

• Research and development in digital IC implementation for machine learning and/or deep neural network applications;
• Develop test methodology and test plans based on logic design specifications;
• Module level and/or top level verification for SoC system;
• Independently handle test environment set up;
• Test case creation/generation and code coverage analysis.

• Research and development in digital IC/FPGA implementation for machine learning and/or deep neural network applications;
• System functional specification, resources evaluation, IC architecture definition, micro-architecture specification and review, RTL implementation, logic synthesis and timing analysis for digital IC design;
• Micro-architecture design, RTL coding for FPGA implementation and prototyping;
• Optimization for hardware design implementation;
• Module-level and/or top-level verification.

• Develop Deep Neural Network machine learning-based algorithm for computer vision applications (object detection, pattern recognition, segmentation, background modelling, etc.);
• Data set training and performance evaluation;
• Hardware performance modelling and simulation;
• Neural network algorithm optimization research;
• Software, firmware and demo system development.

• Design and optimize Analog/Mixed signal circuit blocks;
• Participate circuit testing and evaluations; and
• Maintain accurate and complete documentation of work.