AI Computer Vision Engineer | SDKs, Deployment & Performance

Zebra Technologies; Greater London; On-site; GBP 90,000 - 120,000; Full time

Benefits offered by this job

12% annual cash incentive

Job summary

Zebra Technologies in London is seeking a Computer Vision Engineer to bridge ML research with production systems. You will design, implement, and maintain high-performance SDKs in C++, port and deploy models across edge SoCs, CPUs, and GPUs, and optimize performance for diverse hardware.

You will also automate testing and deployment with Python, contribute to CI/CD pipelines, and collaborate with hardware teams to ensure robustness from edge to cloud.

Qualifications

• Bachelor's or Master's degree in Computer Science, Electronics Engineering, or related field.
• Deep understanding of C++14/17/20, including STL, memory management, and multi-threading.
• Strong ability to write clean Python for automation and data processing.
• Hands-on experience with ML inference frameworks such as SNPE/QNN, OpenVino, TensorRT, TensorFlow Lite.
• Familiarity with Docker for reproducible environments.

Responsibilities

• SDK Development: design and maintain high-performance SDKs for computer vision features.
• Model Deployment: port and deploy ML models across Qualcomm SoCs, Intel CPUs, NVIDIA GPUs.
• Performance Optimization: use hardware toolkits to maximize throughput while keeping accuracy.
• Evaluation & Benchmarking: test models on target hardware against metrics.
• Automation: build and maintain Python CI/CD scripts to streamline testing and deployment.

Skills

C++14/17/20

Python

Docker

Model deployment

Multi-threading

Education

Bachelor's degree in Computer Science or related field

Master's degree (preferred)

Tools

SNPE/QNN

OpenVino

TensorRT

TensorFlow Lite

Job description

Zebra Technologies in London is seeking a Computer Vision Engineer to bridge ML research with production systems. You will design, implement, and maintain high-performance SDKs in C++, port and deploy models across edge SoCs, CPUs, and GPUs, and optimize performance for diverse hardware.

You will also automate testing and deployment with Python, contribute to CI/CD pipelines, and collaborate with hardware teams to ensure robustness from edge to cloud.