Are you excited by the challenge of developing algorithms that enable machines to interpret and make sense of visual data?

Do you excel at creating computer vision systems that recognize, analyze, and respond to real-world environments?

If you’re ready to apply advanced vision technology in robotics, autonomous systems, or industrial applications, our client has the ideal role for you.

We’re looking for a Computer Vision Engineer (aka The Visual Intelligence Architect) to design and implement vision systems that empower machines to see and interact intelligently.As a Computer Vision Engineer at our client, you’ll work with multidisciplinary teams to build solutions that process visual information, detect objects, and make real-time decisions.

Your expertise in image processing, machine learning, and algorithm development will be essential in delivering reliable and scalable vision systems that can perform in various dynamic environments.Key Responsibilities: Develop and Optimize Computer Vision Algorithms: Design, develop, and optimize algorithms for object detection, tracking, recognition, and segmentation.

You’ll leverage deep learning and traditional vision techniques to build efficient solutions for real-world applications.

Implement and Train Machine Learning Models: Create and train machine learning models to interpret and classify visual data.

You’ll use frameworks like TensorFlow and PyTorch to build models that generalize well and perform accurately.

Integrate Vision Systems into Robotics and Automation Platforms: Collaborate with robotics, automation, and software teams to integrate vision systems into operational workflows.

You’ll ensure that vision data flows seamlessly to other system components for responsive, real-time performance.

Perform Calibration and Testing for Vision Hardware: Calibrate and test cameras, sensors, and optics to ensure precise data capture and interpretation.

You’ll work with LIDAR, RGB-D, stereo cameras, or other sensors, ensuring compatibility with system requirements.

Optimize for Real-Time Processing and Embedded Applications: Adapt algorithms to run efficiently on edge devices, embedded systems, or specialized hardware like GPUs.

You’ll focus on achieving high-speed, low-latency performance for real-time applications.

Collaborate on Data Collection and Annotation Processes: Work with data scientists and annotation teams to collect and prepare labeled datasets for training models.

You’ll refine datasets to improve accuracy and robustness in various conditions.

Stay Updated on Computer Vision Advancements: Continuously research new techniques, tools, and methodologies in computer vision and machine learning.

You’ll incorporate the latest advancements to enhance system capabilities and stay at the cutting edge of technology.