Computer Vision Engineer

Augusta Hitech; Northern (KY); Hybrid; USD 110,000 - 170,000; Full time

Job summary

Augusta Hitech in the United States seeks a Computer Vision Engineer to design, develop, and deploy real-time defect detection systems for industrial automation pipelines. You will bridge ML models and manufacturing hardware, integrating high-speed cameras, IPCs, and PLCs to capture, process, and label data at high throughput, ensuring zero-defect quality on the production floor.

You will implement robust object detection and image classification for micro-defects on fast lines, calibrate

Qualifications

• Experience with real-time defect detection in industrial automation.
• Hands-on with high-speed imaging hardware and IPC/PLC integration.
• Proficient in deploying edge AI models on NVIDIA Jetson or x86 IPCs.

Responsibilities

• Design and implement robust object detection and image classification for micro-defects on fast-moving lines.
• Integrate high-speed cameras, lighting, and optics for optimal image quality.
• Establish low-latency pipelines between vision systems and PLCs to trigger sorting in real-time.
• Optimize models for edge deployment using TensorRT/OpenVINO to meet cycle-time targets.
• Develop automated data collection and labeling pipelines to improve model accuracy.
• Troubleshoot camera triggering, lighting fluctuations, and model drift on the factory floor.

Skills

C++

Computer Vision

PyTorch

Tensor

Tools

OpenCV

TensorRT

OpenVINO

Job description

Required Skills: C++, Computer Vision, PyTorch, Tensor

Country: United States

Role Summary

We are seeking a highly skilled Computer Vision Engineer to design, develop, and deploy real-time defect detection systems for our industrial automation pipelines. In this role, you will bridge the gap between advanced machine learning models and physical manufacturing hardware. You will be responsible for integrating high-speed cameras, Industrial PCs (IPCs), and Programmable Logic Controllers (PLCs) to capture, process, and label data at high throughput, ensuring zero-defect quality control on the production floor.

Key Responsibilities

Design and implement robust object detection and image classification algorithms specifically tuned for identifying micro-defects on fast-moving production lines.

Integrate and calibrate high-speed industrial cameras, specialized lighting, and optical setups to ensure optimal image quality for the vision models.

Establish low-latency communication pipelines between the computer vision systems and PLCs to trigger physical sorting or rejection mechanisms in real-time.

Optimize deep learning models for edge deployment using hardware acceleration to meet strict cycle-time requirements.

Develop and maintain automated data collection and labeling pipelines to continuously feed and improve the accuracy of the defect detection models.

Troubleshoot system performance on the manufacturing floor, addressing issues related to camera triggering, lighting fluctuations, and model drift.

Required Programming Languages

C++: Essential for writing highly optimized, multithreaded applications necessary for real-time, low-latency image processing and inferencing on the edge.

Python: Required for rapidly prototyping, training, testing, and refining deep learning models, as well as managing the data processing and labeling scripts.

Structured Text / Ladder Logic (Familiarity): While not strictly required to be an expert, a working knowledge of IEC 61131-3 PLC programming languages is crucial for effectively communicating and designing handshakes with automation engineers.

Required Experience & Technical Skills

High-Speed Imaging Hardware: Proven hands-on experience with industrial camera interfaces (GigE Vision, CoaXPress, Camera Link), frame grabbers, and industrial optics.

Industrial Communication Protocols: Experience seamlessly passing data between Windows/Linux edge computers and PLCs using protocols like OPC UA, Modbus TCP/IP, Ethernet/IP, or PROFINET.

Computer Vision & Deep Learning Frameworks: Deep practical knowledge of traditional computer vision (OpenCV) alongside modern deep learning frameworks (PyTorch, TensorFlow) and architectures suited for fast inference (e.g., YOLO variants).

Hardware Acceleration & Edge AI: Proven ability to deploy models onto industrial hardware (NVIDIA Jetson, x86 IPCs) using optimization toolkits like TensorRT or OpenVINO to maximize frame rates.

Real-Time System Design: Experience dealing with the complexities of concurrent processing, memory management, and preventing frame drops in continuous, 24/7 manufacturing environments.

Data Pipeline Management: Familiarity with tools and best practices for versioning datasets, managing synthetic data generation, and coordinating efficient image labeling workflows for quality control.

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