Embedded AI/ML Engineer — Computer Vision & Edge

W3Global; Manchester (NH); On-site; USD 120,000 - 160,000; Full time

Job summary

Our Client in the United States seeks a skilled AI/ML Software Engineer to develop and optimize image recognition algorithms for embedded edge platforms. You will adapt models such as YOLOv8, Mask R-CNN, and DeepSORT for hardware-constrained environments, and create synthetic labeled data to improve robustness.

You will collaborate with AI researchers, software and hardware engineers to ensure performance, latency, and power targets are met.

Qualifications

• BS in CS/EE/Math/Physics or related field.
• Experience with ML frameworks: PyTorch, TensorFlow, or ONNX.
• Proficiency with OpenCV, TensorRT, or OpenVINO.
• Strong Python and/or C++ programming skills.
• Experience deploying AI models on embedded/edge hardware (Jetson/NUC/AMD Ryzen Embedded).
• Experience evaluating ML algorithms with quantitative metrics.
• Strong analytical and problem-solving abilities.
• Ability to work in multidisciplinary teams.

Responsibilities

• Assess, modify, and optimize image recognition algorithms for embedded/edge platforms.
• Design, develop and implement synthetic labeled training datasets.
• Generate physics-based synthetic imagery for realistic sensor performance.
• Evaluate AI model performance using precision, recall, and other metrics.
• Collaborate with AI researchers, software and hardware engineers to meet objectives.
• Optimize AI models for embedded hardware balancing performance and power.
• Research and implement emerging AI/edge deployment tech.
• Participate in software design, testing, and continuous improvement.

Skills

Python

C++

Analytical thinking

Problem solving

Team collaboration

Education

Bachelor's degree in CS/EE/Math/Physics

Master’s degree or 5+ years imaging/AI experience

Tools

PyTorch

TensorFlow

ONNX

OpenCV

TensorRT

OpenVINO

NVIDIA Jetson

Intel NUC

AMD Ryzen Embedded

Job description

Our Client in the United States seeks a skilled AI/ML Software Engineer to develop and optimize image recognition algorithms for embedded edge platforms. You will adapt models such as YOLOv8, Mask R-CNN, and DeepSORT for hardware-constrained environments, and create synthetic labeled data to improve robustness.

You will collaborate with AI researchers, software and hardware engineers to ensure performance, latency, and power targets are met.