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Principal AI Engineer
Analog Devices
On-site
Boston, MA, United States
Full-time
$148,500 -
$222,750
About Analog Devices
Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™.
About the Role
The Edge AI team is seeking a Principal AI Engineer to lead the development of next-generation AI building blocks for constrained hardware devices. This role focuses on creating and optimizing AI models and frameworks that push the boundaries of machine learning on resource-limited platforms. As a technical leader, you will take responsibility for the entire lifecycle of AI models and solutions, from development and optimization to benchmarking, implementation, and delivery.
Qualifications
Master’s degree or equivalent experience.
8+ years of industry experience in AI/ML model development, optimization, and deployment.
Strong expertise in AI model optimization techniques, including quantization, pruning, and other performance-enhancing methods.
Solid understanding of hardware benchmarking and the trade-offs of deploying AI on constrained devices.
Proficiency in Python and C++ for AI model development and integration.
Familiarity with AI frameworks such as TensorFlow, PyTorch, or ONNX, and their deployment across various hardware targets.
Experience with edge hardware platforms, including CPUs, NPUs, and accelerators.
Proven ability to lead major technical projects and collaborate across multidisciplinary teams.
Preferred Skills:
Experience with designing custom AI architectures for edge devices.
Knowledge of Analog Devices hardware platforms or similar embedded environments.
Strong communication and leadership skills, with a demonstrated ability to mentor and grow engineering talent.
8+ years of industry experience in AI/ML model development, optimization, and deployment.
Strong expertise in AI model optimization techniques, including quantization, pruning, and other performance-enhancing methods.
Solid understanding of hardware benchmarking and the trade-offs of deploying AI on constrained devices.
Proficiency in Python and C++ for AI model development and integration.
Familiarity with AI frameworks such as TensorFlow, PyTorch, or ONNX, and their deployment across various hardware targets.
Experience with edge hardware platforms, including CPUs, NPUs, and accelerators.
Proven ability to lead major technical projects and collaborate across multidisciplinary teams.
Preferred Skills:
Experience with designing custom AI architectures for edge devices.
Knowledge of Analog Devices hardware platforms or similar embedded environments.
Strong communication and leadership skills, with a demonstrated ability to mentor and grow engineering talent.
Responsibilities
Lead the design, development, and optimization of AI models for deployment on Edge AI hardware accelerators.
Drive hardware benchmarking efforts to evaluate model performance and resource utilization.
Implement techniques such as quantization, pruning, and other optimization methods to enable efficient AI execution on edge devices.
Collaborate with cross-functional teams to integrate AI solutions into hardware and software platforms.
Take ownership of the full model lifecycle, including prototyping, implementation, validation, and delivery.
Provide thought leadership and technical mentorship to team members, fostering innovation and growth.
Stay updated on the latest developments in AI and hardware optimization techniques and apply them to improve performance and efficiency.
Drive hardware benchmarking efforts to evaluate model performance and resource utilization.
Implement techniques such as quantization, pruning, and other optimization methods to enable efficient AI execution on edge devices.
Collaborate with cross-functional teams to integrate AI solutions into hardware and software platforms.
Take ownership of the full model lifecycle, including prototyping, implementation, validation, and delivery.
Provide thought leadership and technical mentorship to team members, fostering innovation and growth.
Stay updated on the latest developments in AI and hardware optimization techniques and apply them to improve performance and efficiency.
