What distinguishes a computer vision scientist from a computer vision engineer?

With a bachelor's degree in computer science and over 10 years of experience in developing algorithms and implementing AI models for cameras across diverse industries, I've gained recognition for my practical achievements. Despite lacking a PhD, I stay current in the rapidly evolving field of computer vision through self-directed learning. While some suggest pursuing a PhD, I find fulfillment as a computer vision specialist, leveraging hands-on experience to contribute to the field. The decision to pursue a PhD will be guided by personal aspirations and alignment with career goals, considering my existing expertise and continuous learning already position me as a valuable professional in computer vision.

Computer Vision is the field where image data is used to capture information or is manipulated to generate new information. Problem domains such as classification, object detection, and semantic segmentation are for capturing information from image data. Image deformations and generative-adversarial networks are examples of data manipulation. Most computer vision engineers/scientists work with either of these. Each problem domain contains a vast amount of information, therefore, specialization is required to create cutting-edge products and applications. The field changes rapidly, therefore whether you have a BSc or PhD, you need to learn continually by following the latest research that is relevant to the problem domain that you work with.

Vision scientist is the guy developing fundamentally new paradigms, designing new architectural backbones, publishing... Vision engineer is more focused on setting up image acquisition, looking at quality of actual data at hand (not standard datasets), helping design annotation tools & ensuring quality of labels, finding compute & training - may often implement papers & be surprised at how short they fall off claims & expectations...

If you're into artificial intelligence (AI) but math isn't exactly your jam, LIKE ME, don't sweat it. You've still got a solid shot at making waves in the AI world. Think about it like this: If you're more about getting things done and like seeing your work in action, a Computer Vision Engineer might be your thing. It's less about numbers and more about making those AI ideas work in real life. But if you're up for a challenge and willing to brush up on your math skills, diving into the nitty-gritty of algorithms as a Computer Vision Scientist could be super rewarding. Whatever path you choose, your passion for AI is what really counts. Go where you feel you'll shine! Diversity is key.

Computer vision scientists require a solid mathematical foundation, research skills, and proficiency in Python, MATLAB, or C++. They focus on advancing theoretical aspects, conducting experiments, and developing algorithms, with deep learning knowledge and image processing expertise. In contrast, computer vision engineers need strong programming skills in Python, C++, or Java, applying solutions in real-world scenarios. They demand hands-on experience, software development skills, and optimization proficiency. Problem-solving is crucial for integrating computer vision into projects, and staying updated on industry advancements is vital for both roles.

A CV-scientist is more likely to be on the RnD side, where you need to solve a problem - math orientation, available and relevant frameworks and tools (python, pytorch, models zoo) etc The CV-engineer will tend to be familiar with existing SW, HW and overall design of the system (docker, cloud infra, gpu & full HW spec if relevant). Together they should bring the original solution from the notebook to production environment.

Computer vision scientists uses programming languages like Python, MATLAB, and C++ for algorithm development, often using deep learning frameworks such as TensorFlow and PyTorch. Image processing tasks are facilitated by OpenCV, while mathematical operations are handled with tools like NumPy and SciPy. In contrast, computer vision engineers, in addition to programming proficiency in Python, C++, or Java, utilize cloud computing platforms like AWS or Azure for scalable infrastructure. They deploy solutions using specialized computer vision libraries and optimize performance with tools like profilers. IDEs such as Visual Studio and collaboration tools like Jira and Slack enhance the software development process.

In short, you'd expect the CV-scientist to be responsible for the system's accuracy and performance while the CV-engineer on the integration part into the prod environment. In commercial enterprises, always remember to solve the business problem, rather than chasing novelty for the sake of novelty.

An engineer will use the camera, and the associated image processing, as a sensor that is a useful part of a large system. A computer scientist will attempt to mimic a human.

The positions' descriptions differ greatly based on the company and the team you are at. In some cases you are expected to do end-to-and, from data review, through model training, to integration into production space, and in others you can focus on a specific task.

For scientists, understanding computer graphics enhances algorithm development. Active participation in open-source communities keeps them abreast of the latest research. Proficiency in GPU programming and parallel computing optimizes performance. On the engineering side, cloud computing proficiency aids in deploying and scaling computer vision applications. Engineers benefit from knowledge of hardware architectures, ensuring efficient algorithm execution. Staying updated on industry advancements is crucial for both roles. The dynamic nature of computer vision necessitates continuous learning, fostering a synergy of technical and interpersonal skills for success.