Jabil
EdgeAIEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Edge AI Engineer at Jabil. Skills: Edge AI/ML development, Embedded systems optimization, Deep learning frameworks for edge, Python, C++. Design, develop, and optimize AI/ML models for deployment on resource-constrained edge devices. Implement efficient inference engines and real-time data processing pipelines at the edge”
Industry & Context.
problem-solving skills
What They're Looking For.
Must Have
3+ years of experience in AI/ML development, with a focus on embedded systems or edge computing (e. g. Raspberry Pi), Proficiency in programming languages such as Python and C++, Hands-on experience with deep learning frameworks like TensorFlow Lite, PyTorch Mobile, HailoRT or similar, Solid understanding of neural network architectures, computer vision, and/or natural language processing, Experience with optimizing AI models for edge deployment (e. g. , quantization, pruning, knowledge distillation), Familiarity with various edge computing platforms and hardware accelerators (e. g. , Hailo, NVIDIA Jetson, Google Coral, specialized ASICs)
Nice to Have
Experience with real-time operating systems (RTOS) and embedded software development is a plus
What You'll Do.
and optimize AI/ML models for deployment on resource-constrained edge devices
Implement efficient inference engines and real-time data processing pipelines at the edge
Work with various sensor data (e. g.
motion) to develop robust AI applications
Optimize model performance for power consumption
and memory footprint on target hardware
Develop and implement MLOps strategies for continuous integration
and monitoring of edge AI models
Conduct performance benchmarking and validation of edge AI solutions
Stay abreast of the latest advancements in edge computing
and machine learning algorithms
How You'll Work.
Team & Collaboration
Collaborate with hardware engineers to select appropriate edge computing platforms and ensure seamless integration of AI solutions; Provide technical guidance and support to cross-functional teams
Communication Scope
Excellent communication and interpersonal skills
Full Job Description
At Jabil (NYSE: JBL), we are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe. Job Responsibilities** * Design, develop, and optimize AI/ML models for deployment on resource-constrained edge devices (e.g., microcontrollers, specialized AI accelerators). * Implement efficient inference engines and real-time data processing pipelines at the edge. * Work with various sensor data (e.g., vision, audio, motion) to develop robust AI applications. * Collaborate with hardware engineers to select appropriate edge computing platforms and ensure seamless integration of AI solutions. * Optimize model performance for power consumption, latency, and memory footprint on target hardware. * Develop and implement MLOps strategies for continuous integration, deployment, and monitoring of edge AI models. * Conduct performance benchmarking and validation of edge AI solutions. * Stay abreast of the latest advancements in edge computing, AI hardware, and machine learning algorithms. * Provide technical guidance and support to cross-functional teams. * Document designs, architectures, and implementation details thoroughly. Job Qualifications** * Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related quantitative field. * 3+ years of experience in AI/ML development, with a strong focus on embedded systems or edge computing (e.g. Raspberry Pi) * Proficiency in programming languages such as Python and C++. * Hands-on experience with deep learning frameworks like TensorFlow Lite, PyTorch Mobil
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