Company Overview

At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.

Department Overview

R&D

Job Description:

We are seeking a skilled and experienced Embedded AI Engineer to join our team. The successful candidate will be responsible for the development and integration of AI models across diverse hardware platforms, with a strong focus on optimizing for edge device deployment. This role is responsible for delivering high-quality, scalable, and robust AI solutions.

Key Responsibilities

Your responsibilities will cover the entire lifecycle of an AI model, from receiving it from the research team to ensuring its efficient operation on the end device.

1.

AI Model Optimization & Deployment:

• Analyze trained AI models (from Tensor Flow, Py Torch) to identify performance bottlenecks and resource requirements (memory, compute).

• Convert models into inference-optimized formats such as ONNX, TensorRT, and TFLite.

• Apply advanced optimization techniques, including quantization (INT8/FP16) and pruning, to reduce model size and accelerate processing speed while maintaining accuracy.

• Profile and benchmark model performance on target hardware (e.g., NVIDIA Jetson, ARM CPUs) to ensure latency and throughput criteria are met.

Application Software Development:

• Build high-performance applications and libraries in C++/Python to load, manage, and execute AI models in both Linux and Windows environments.

Research & Improvement:

• Stay current with the latest technologies, algorithms, and tools in embedded AI and efficient machine learning.

• Research and experiment with new AI models to evaluate their feasibility and potential for product application.

• Participate in troubleshooting, debugging, and continuously improving deployed AI systems to enhance performance and reliability.

Collaboration & Technical Support:

• Collaborate closely with AI/ML scientists to understand model architectures and deployment requirements.

• Work with hardware engineering teams to leverage specialized on-chip acceleration features.

• Support other teams (such as QA and Product) in integrating and testing AI solutions.

• Develop end-to-end data processing pipelines, from pre-processing input data (images, video) to post-processing model outputs.

• Create and maintain unit and integration tests to ensure the stability and accuracy of AI features.

Basic Requirements:

• Education: Bachelor's or Master’s degree in Computer Science, Computer Vision, or a closely related technical field.

• Experience: 3+ years of relevant professional experience in AI/Embedded software development.

Preferred Qualifications:

• Relevant academic coursework, significant projects, or internships in Artificial Intelligence/Machine Learning or hardware optimization.

• Active participation in technology communities, open-source contributions, or successful hackathon experiences.

• Programming: Strong proficiency in C/C++ and Python. Experience with CUDA programming is a strong plus.

• Edge Deployment (Merged): Hands-on experience deploying AI models on Edge Computing platforms from major vendors such as NVIDIA (Jetson series) or Ambarella (CVflow architecture), utilizing optimization tools like TensorRT, TFlite, and ONNX.

• OS: Solid experience in developing and deploying applications on Linux (Ubuntu/Embedded) and Windows operating systems.

• Deep Learning Frameworks: Strong proficiency in Py Torch or Tensor Flow, with the ability to analyze model graphs, layers, and operations for optimization purposes.

• Optimization Techniques: In-depth knowledge of model compression techniques, specifically Quantization (INT8/FP16) and Pruning. Experience with Post-Training Quantization (PTQ) or Quantization-Aware Training (QAT) is a plus.

• Hardware Profiling: Experience profiling and benchmarking AI models to identify bottlenecks (latency/throughput) using tools like NVIDIA Nsight Systems, Nsight Compute,trtexec, or Jtop.

• Computer Architecture: Solid understanding of memory management, GPU/CPU parallelism, and hardware constraints on embedded systems.

• Language: Good command of the English language, both written and verbal.

Travel Requirements:

None

Relocation Provided

None

Position Type:

Experienced

Referral Payment Plan

Yes

EEO Statement

Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or belief, sex, sexual orientation, gender identity, national origin, disability, veteran status or any other legally-protected characteristic.

We are proud of our people-first and community-focused culture, empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If you’d like to join our team but feel that you don’t quite meet all of the preferred skills, we’d still love to hear why you think you’d be a great addition to our team.