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Infocomm TechnologyISCO 2511

Computer vision engineer

Computer vision engineers research, design, develop, and train artificial intelligence algorithms and machine learning primitives that understand the content of digital images based on a large amount of data. They apply this understanding to solve different real-world problems such as security, autonomous driving, robotic manufacturing, digital image classification, medical image processing and diagnosis, etc.

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Rate 5 of the 32 essential skills of this occupation (European ESCO data). No account needed.

normalise data

conduct literature research

utilise computer-aided software engineering tools

interpret current data

establish data processes

About computer vision engineer

As a computer vision engineer, you will develop cutting-edge artificial intelligence and machine learning systems that enable computers to understand, interpret, and act on visual information from images and video. Your work involves researching novel algorithms, designing neural networks, processing large datasets, and creating solutions that power applications across diverse fields including autonomous vehicles, medical imaging, industrial quality control, security systems, and robotics. You'll tackle complex challenges in image recognition, object detection, scene understanding, and visual analysis.

Your daily responsibilities include writing and optimizing code in Python and other languages, training and fine-tuning machine learning models on large datasets, conducting literature research to stay current with advances, testing algorithms against real-world data, and documenting your work. You'll perform extensive data preprocessing, normalize large datasets, apply statistical analysis techniques, and use computer-aided engineering tools to validate your solutions. This role requires strong problem-solving abilities, mathematical aptitude, and the ability to bridge theoretical research with practical applications.

Career prospects are exceptional in Poland's growing tech and AI sector, with significant demand from companies in autonomous systems, digital transformation, and artificial intelligence startups. You can specialize in specific domains like medical imaging, autonomous driving, or industrial automation, advance to research roles, or lead AI development teams. The field is rapidly evolving, offering continuous learning opportunities and competitive compensation.

Key Work Functions

Core areas of responsibility for a computer vision engineer.

1

Algorithm Research and Development

  • Research novel computer vision and machine learning algorithms through literature review
  • Design and prototype image recognition and object detection algorithms
  • Experiment with different neural network architectures for specific vision tasks
  • Conduct scientific research and publish findings on computer vision advances
2

Data Processing and Preparation

  • Collect, organize, and preprocess large datasets of images for training
  • Normalize data and perform feature engineering for improved model performance
  • Perform digital image processing including filtering, enhancement, and segmentation
  • Validate data quality and handle missing or corrupted samples
3

Model Training and Optimization

  • Train deep learning models using Python and machine learning frameworks
  • Fine-tune pre-trained models for specific computer vision applications
  • Optimize model performance, reduce computational complexity, and improve inference speed
  • Apply statistical analysis and hyperparameter tuning techniques
4

Implementation and Testing

  • Develop production-ready computer vision systems and applications
  • Test algorithms against real-world datasets and edge cases
  • Integrate computer vision models into larger software systems and frameworks
  • Deploy models to production environments and monitor performance
5

Software Development and Documentation

  • Write clean, efficient code in Python and other programming languages
  • Use integrated development environments and version control systems
  • Utilize computer-aided software engineering tools for system design and testing
  • Document code, algorithms, and methodologies for team collaboration

European Skills Framework

Skills and knowledge areas required for this occupation based on European classification.

Essential (32)

computer simulationcomputer simulationA programme run on a computer that represents dynamic responses of a system to explore a mathematical model behaviour, using a model of a real system, composed of mathematical equations.computer programmingcomputer programmingThe techniques and principles of software development, such as analysis, algorithms, coding, testing and compiling of programming paradigms (e.g. object oriented programming, functional programming) a...machine learningmachine learningThe principles, methods and algorithms of machine learning, a subfield of artificial intelligence. Common machine learning models such as supervised or unsupervised models, semi- supervised models and...image recognitionimage recognitionA process designed to detect and identify a feature or object in an image or video. This process is used in medical imaging, security surveillance or defect detection, among other fields. Key techniqu...data engineeringdata engineeringThe process of developing and constructing systems for implementing data collection and analysis at large scale.scientific computingscientific computingThe interdisciplinary field between computer science, mathematics and engineering. It concerns the employment of technical approaches and theoretical frameworks, and the use of computers, to address i...digital twin technologydigital twin technologyModel designed to generate a virtual representation of an object or system updated from real-time data. The virtual representation process is through the combination of data and technology simulation,...statisticsstatisticsThe study of statistical theory, methods and practices such as collection, organisation, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of ...integrated development environment softwareintegrated development environment softwareThe suite of software development tools for writing programs, such as compiler, debugger, code editor, code highlights, packaged in a unified user interface, such as Visual Studio or Eclipse.digital image processingdigital image processingThe different aspects and practices of image processing and manipulation like image interpolation, aliasing, image enhancement, contrast stretching, histogram processing and equalization, singular val...Python (computer programming)Python (computer programming)The techniques and principles of software development, such as analysis, algorithms, coding, testing and compiling of programming paradigms in Python.principles of artificial intelligenceprinciples of artificial intelligenceThe artificial intelligence theories, applied principles, architectures and systems, such as intelligent agents, multi-agent systems, expert systems, rule-based systems, neural networks, ontologies an...data sciencedata scienceThe field of study that deals with big amount of data using AI techniques such as machine learning algorithms to predict patterns and obtain useful information to make business decisions.normalise datanormalise dataReduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.conduct literature researchconduct literature researchConduct a comprehensive and systematic research of information and publications on a specific literature topic. Present a comparative evaluative literature summary.utilise computer-aided software engineering toolsutilise computer-aided software engineering toolsUse software tools (CASE) to support the development lifecycle, design and implementation of software and applications of high-quality that can be easily maintained.interpret current datainterpret current dataAnalyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in ar...establish data processesestablish data processesUse ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.execute analytical mathematical calculationsexecute analytical mathematical calculationsApply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.apply statistical analysis techniquesapply statistical analysis techniquesUse models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.perform dimensionality reductionperform dimensionality reductionReduce the number of variables or features for a dataset in machine learning algorithms through methods such as principal component analysis, matrix factorization, autoencoder methods, and others.develop computer vision systemdevelop computer vision systemApply and combine different computer vision tools and methods such as image acquisition, image processing, image segmentation and classification, detection, etc. in one system to allow computers to ex...handle data sampleshandle data samplesCollect and select a set of data from a population by a statistical or other defined procedure.perform data cleansingperform data cleansingDetect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.implement data quality processesimplement data quality processesApply quality analysis, validation and verification techniques on data to check data quality integrity.develop software prototypedevelop software prototypeCreate a first incomplete or preliminary version of a piece of software application to simulate some specific aspects of the final product.use software librariesuse software librariesUtilise collections of codes and software packages which capture frequently used routines to help programmers simplify their work.manage data collection systemsmanage data collection systemsDevelop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.deliver visual presentation of datadeliver visual presentation of dataCreate visual representations of data such as charts or diagrams for easier understanding.report analysis resultsreport analysis resultsProduce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as...define technical requirementsdefine technical requirementsSpecify technical properties of goods, materials, methods, processes, services, systems, software and functionalities by identifying and responding to the particular needs that are to be satisfied acc...develop data processing applicationsdevelop data processing applicationsCreate a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.
Optional (18) — show
mathematical modellingmathematical modellingThe process of establishing a mathematical representation problem, such as equations, of a real-word to provide insights, discover new features about the problematic scenario, better understand the or...digital systemsdigital systemsSystems to store, process and spread information in digital form. They are hardware, software and networks, as well as the ways in which these can be employed.image formationimage formationThe principles and factors determining the formation of an image such as geometry, radiometry, photometry, sampling and analog to digital conversion.resource description framework query languageresource description framework query languageThe query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).computer graphicscomputer graphicsThe field that merges approaches from computer science and design. It involves the process of creating images, 3D models generation, as well as the rendering, texturing and lighting them. Its applicat...cognitive computingcognitive computingThe interdisciplinary field between cognitive science and computer science that involves simulating human thinking processes through a computerised approach. It makes use of algorithms for data mining...signal processingsignal processingThe algorithms, applications and implementations that deal with the processing and transferring of information through analog or digital frequencies.state estimationstate estimationThe process of fusing input data measurements and a mathematical model to determine the internal state of an energy system.quantum computingquantum computingThe branch of computer science that follows the principles of quantum theory. It uses subatomic particles which are allowed to exist under more than one state thanks to quantum bits, or qubits.deep learningdeep learningThe principles, methods and algorithms of deep learning, a subfield of artificial intelligence and machine learning. Common neural networks like perceptrons, feed-forward, backpropagation, and convolu...conduct scholarly researchconduct scholarly researchPlan scholarly research by formulating the research question and conducting empirical or literature research in order to investigate the truth of the research question.define data quality criteriadefine data quality criteriaSpecify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.conduct quantitative researchconduct quantitative researchExecute a systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques.create data modelscreate data modelsUse specific techniques and methodologies to analyse the data requirements of an organisation's business processes in order to create models for these data, such as conceptual, logical and physical mo...design user interfacedesign user interfaceCreate software or device components which enable interaction between humans and systems or machines, using appropriate techniques, languages and tools so as to streamline interaction while using the ...use markup languagesuse markup languagesUtilise computer languages that are syntactically distinguishable from the text, to add annotations to a document, specify layout and process types of documents such as HTML.debug softwaredebug softwareRepair computer code by analysing testing results, locating the defects causing the software to output an incorrect or unexpected result and remove these faults.perform data miningperform data miningExplore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.

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Frequently asked questions

What does a computer vision engineer do?

Computer vision engineers research, design, develop, and train artificial intelligence algorithms and machine learning primitives that understand the content of digital images based on a large amount of data. They apply this understanding to solve different real-world problems such as security, autonomous driving, robotic manufacturing, digital image classification, medical image processing and diagnosis, etc.

What skills does a computer vision engineer need?

A computer vision engineer needs 32 essential skills, including computer simulation, computer programming, machine learning, image recognition, data engineering.

Will AI replace computer vision engineer jobs?

The AI disruption score for computer vision engineer is 82/100 (very high risk). Skills most exposed to automation: image recognition, normalise data, perform data mining. Most resilient: quantum computing, machine learning, digital twin technology.

AI Replacement Risk

82/ 100
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Quick Facts

SectorInfocomm Technology
ISCO GroupInformation and communications technology professionals
Essential Skills32

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