Senior Engineer II-Applications- AI/ML

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Job Description:

Microchip Technology Inc. is seeking Senior Applications Engineer to support the development of Edge AI / Machine Learning demonstrations on Microchip MCU, MPU, and FPGA platforms.
This role focuses on creating, validating, and documenting reference designs and demonstrations that enable customers to deploy AI/ML workloads on resource-constrained embedded systems. The successful candidate will work closely with internal engineering, marketing, and field teams to enable customers through high-quality demos, example code, and technical collateral.
 

Join our growing Edge AI team!

Key Responsibilities:

• Develop Embedded applications using Microchip AI offerings demonstrating product capabilities and features set with focus on Edge AI based solutions

• Develop reference solutions and proof of concept demos to assist customers in designing, developing & troubleshooting Edge AI applications like Audio / voice implementation using AI on MCU platforms using TinyML. 

• Attend meetings with various stakeholders to understand the key customer issues and come up with solutions to solve them

• Participate in Development, review and implementation of AI/ML applications, models and algorithms optimized for edge computing environments in our cloud-based development and application space.

• Use AI models in various platforms using TFLite, Tensorflow, ONNX, Pytorch based technologies.

• Provide presentations and trainings on technical topics to customers and other stakeholders

• Provide feedback on improvements in Microchip’s hardware, software, tools and documentation targeting Edge AI solutions.

• Work closely with product management to help define project requirements, milestones, and deliverables.

• Work with software engineers to develop methodologies in gathering, applying and evaluating data for consumption by AI models running on Edge AI hardware.

• Train and tune AI models for efficient execution on hardware platforms.

• Utilize best practices in software development, including code reviews, unit testing, integration and continuous integration.

• Stay up-to-date with the latest advancements in AI/ML and edge computing technologies to ensure our solutions remain at the cutting edge.

• Work in a team environment, developing and fostering a culture of innovation and continuous improvement.

Requirements/Qualifications:

• Bachelor’s, Master’s degree in Computer Science, Electronics Engineering or related field.

• 7+ years of total experience in Embedded Firmware design, development for Bachelor’s and with 2+ years of specific AI/ML based application development.

• Proficient knowledge in using machine learning development languages, tools and frameworks, such as the MPLAB Machine Learning Development Suite and Python AI/ML libraries using TensorFlow/TFLite, Pytorch, Keras, and ONNX.

• Prior experience on any part of audio processing / DSP would be an advantage.

• Prior experience implementing tiny ML class algorithm on ARM M0 to M7 class MCU and / or similar performance DSP would be preferred.

• Solid programming experience in Embedded C and use of Embedded Software frameworks/ solutions and OS fundamentals.

• Good knowledge of one of more communication protocols/interfaces such as I2C, SPI, UART as well as other peripherals such as ADC/DAC, PWM, RTC, WDT, Timer/Counters

• Experience in USB/ Ethernet MAC/ Graphics will be an added advantage

• Application layer software development experience on embedded systems. Ability to translate complex technical concepts into simplified examples.

• Work experience on IDE tools like MPLAB IDE/ IAR Workbench/ Keil MDK/ Atmel Studio or Linux environments

• Exposure with RTOS/ Zephyr would be an added advantage

• Team player with Strong written and verbal communication skills.

• Must be highly motivated. Possess eager attitude toward learning new skills & technology

Preferred Qualifications:

• Experience in TinyML or Edge AI workflows.
  Familiarity with use of computing platforms (AWS, Azure, GCP, OCI) and edge-to-cloud integration.

• Knowledge of data collection, preprocessing, and training neural networks in data centers.

• Solid understanding of edge computing architectures, including IoT devices, embedded systems, and real-time processing.

• Experience with edge computing platforms and optimizing AI/ML models for resource-constrained devices.

• Knowledge of signal processing and analysis in an ML framework.

• Knowledge of security and privacy considerations in edge computing and cyber security development and analysis methods using ISO-21434 workflow requirements.

Travel Time:

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