5,579 Machine Vision jobs in the United Kingdom

• Design, develop, and implement machine learning models for computer vision tasks such as object detection, image segmentation, facial recognition, and pose estimation.
• Process, clean, and augment large datasets of images and videos for model training.
• Train, fine-tune, and evaluate deep learning models using frameworks like TensorFlow, PyTorch, or Keras.
• Optimise model performance for efficiency, accuracy, and scalability, considering deployment constraints.
• Collaborate with data scientists, software engineers, and product managers to define project requirements and integrate ML solutions.
• Stay current with the latest research and advancements in computer vision and deep learning.
• Conduct experiments, document findings, and present results to technical and non-technical audiences.
• Contribute to the continuous improvement of our ML infrastructure and MLOps practices.
• Troubleshoot and debug ML models and pipelines.

• Master's or PhD degree in Computer Science, Machine Learning, or a related quantitative field, or equivalent practical experience.
• Proven experience in developing and deploying computer vision models.
• Strong programming skills in Python and proficiency with relevant ML libraries (e.g., OpenCV, scikit-image).
• Hands-on experience with deep learning frameworks (TensorFlow, PyTorch).
• Solid understanding of machine learning theory, algorithms, and best practices.
• Experience with cloud platforms (AWS, Azure, GCP) for ML development is a plus.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and teamwork abilities, effective in a hybrid work setting.

• Develop, train, and evaluate machine learning models for various computer vision applications.
• Implement and optimize deep learning algorithms for tasks such as image classification, object detection, and segmentation.
• Design and build robust ML pipelines for data preprocessing, model training, and deployment.
• Collaborate with researchers and engineers to define project requirements and translate them into technical solutions.
• Stay up-to-date with the latest research and advancements in computer vision and machine learning.
• Conduct experiments, analyse results, and present findings to technical and non-technical audiences.
• Optimise model performance for efficiency, accuracy, and scalability.
• Contribute to the development of intellectual property and research publications.
• Work within a remote, agile environment, actively participating in team discussions and code reviews.
• Ensure code quality, maintainability, and adherence to best practices.

• MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Proven experience in developing and deploying machine learning models, with a focus on computer vision.
• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Keras, OpenCV).
• Strong understanding of deep learning architectures (CNNs, RNNs, Transformers).
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration skills, particularly in a remote setting.
• Ability to work independently and manage projects effectively.

• Design, implement, and deploy state-of-the-art computer vision models for tasks such as object detection, image segmentation, facial recognition, and video analysis.
• Develop and optimize deep learning architectures using frameworks like TensorFlow, PyTorch, or Keras.
• Process and manage large-scale image and video datasets, including data augmentation and annotation strategies.
• Collaborate with research scientists to translate novel algorithms into production-ready code.
• Develop robust pipelines for training, evaluating, and monitoring ML models.
• Work on optimizing model performance for various hardware platforms and deployment scenarios.
• Stay current with the latest advancements in computer vision and machine learning research.
• Contribute to the design and architecture of ML systems and infrastructure.
• Collaborate with software engineering teams to integrate ML models into larger applications.
• Document research findings, model designs, and implementation details.

• MSc or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related quantitative field.
• 3+ years of professional experience in machine learning, with a strong focus on computer vision.
• Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of image processing techniques and computer vision algorithms.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Familiarity with cloud platforms (AWS, GCP, Azure) and ML deployment strategies.
• Strong software engineering skills, including version control (Git) and testing.
• Excellent analytical and problem-solving abilities.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

• Design, implement, and train machine learning models for computer vision tasks (e.g., image classification, object detection, segmentation).
• Develop and optimize deep learning architectures using frameworks such as TensorFlow, PyTorch, or Keras.
• Process and manage large datasets of images and videos for model training and evaluation.
• Deploy computer vision models into production environments, ensuring scalability and performance.
• Collaborate with software engineers to integrate ML models into existing applications and platforms.
• Stay current with the latest research and advancements in computer vision and deep learning.
• Conduct experiments and analyze results to improve model accuracy and efficiency.
• Develop and implement robust testing and validation procedures for ML models.
• Work closely with product managers and stakeholders to understand project requirements and translate them into technical specifications.
• Troubleshoot and debug issues related to model performance and deployment.
• Contribute to the development of reusable ML libraries and tools.
• Maintain comprehensive documentation for models, code, and experiments.
• Present research findings and project progress to technical and non-technical audiences.
• Ensure ethical considerations and bias mitigation are addressed in model development.
• Optimize models for deployment on various hardware, including edge devices.

• Master's or PhD in Computer Science, Electrical Engineering, AI, or a related quantitative field.
• Minimum of 3 years of experience in machine learning engineering, with a specialization in computer vision.
• Strong proficiency in Python and ML libraries (e.g., OpenCV, Scikit-learn).
• Hands-on experience with deep learning frameworks (TensorFlow, PyTorch).
• Solid understanding of image processing techniques and algorithms.
• Experience with data augmentation and dataset management.
• Familiarity with cloud platforms (AWS, Azure, GCP) and ML services.
• Knowledge of MLOps principles and tools is a plus.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Ability to work effectively in a collaborative, on-site environment.
• Experience with embedded systems or edge AI is desirable.

• Develop and implement machine learning algorithms for computer vision tasks, including image classification, object detection, segmentation, and tracking.
• Build, train, and evaluate deep learning models using frameworks such as TensorFlow or PyTorch.
• Preprocess and augment large image and video datasets for model training.
• Optimise model performance for inference speed and accuracy on various hardware.
• Collaborate with software engineers to integrate ML models into production systems.
• Conduct research into state-of-the-art computer vision techniques and propose innovative solutions.
• Stay up-to-date with the latest advancements in AI, machine learning, and computer vision.
• Document research, experiments, and model development processes thoroughly.
• Work closely with product teams to understand requirements and translate them into technical solutions.
• Participate in code reviews and contribute to the team's best practices.
• Troubleshoot and debug ML pipelines and models.
• Experiment with different model architectures and hyperparameter tuning.
• Contribute to the development of MLOps pipelines for deployment and monitoring.
• Evaluate and select appropriate tools and technologies for ML projects.
• Present findings and project updates to technical and non-technical stakeholders.

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, AI, or a related quantitative field.
• 2+ years of professional experience in machine learning or computer vision.
• Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, OpenCV, Scikit-learn).
• Strong understanding of deep learning architectures (CNNs, RNNs, Transformers).
• Experience with image and video processing techniques.
• Familiarity with cloud platforms (AWS, Azure, GCP) for ML workloads.
• Solid grasp of data structures, algorithms, and software engineering principles.
• Excellent analytical and problem-solving skills.
• Good communication and teamwork abilities.
• Experience with MLOps practices is a plus.
• Ability to work on complex projects with minimal supervision.
• A keen interest in continuous learning and staying current in the field.
• Experience with data augmentation techniques.
• Understanding of model deployment strategies.

• Design, implement, and optimize machine learning models for computer vision tasks.
• Develop robust data pipelines for image and video data processing and augmentation.
• Train, evaluate, and fine-tune deep learning models using frameworks like TensorFlow or PyTorch.
• Deploy machine learning models into production environments, ensuring scalability and performance.
• Collaborate with cross-functional teams to define project requirements and deliver solutions.
• Stay current with the latest research and advancements in computer vision and machine learning.
• Conduct rigorous testing and validation of ML models to ensure accuracy and reliability.
• Contribute to the development of internal ML platforms and tools.
• Document code, experiments, and model performance thoroughly.
• Participate in code reviews and knowledge-sharing sessions.

• Master's degree or Ph.D. in Computer Science, AI, Machine Learning, or a related quantitative field.
• Proven experience in developing and deploying computer vision models.
• Strong proficiency in Python and relevant ML libraries (e.g., OpenCV, scikit-learn).
• Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Solid understanding of computer vision algorithms and techniques.
• Experience with data preprocessing, augmentation, and feature engineering for image data.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices is a plus.
• Excellent problem-solving skills and the ability to work effectively in a team.
• Strong communication skills to articulate complex technical concepts.
• Ability to commute to Reading, Berkshire, UK for hybrid working arrangements.

Research Engineering Manager

Location - London / Hybrid (3 days onsite, 2 days remote)

What are we looking for:

MicroTECH Global are looking for unique people for a distinctive technology.

Are you a Research Engineering Manager, who has a passion for technology, with a love to lead cutting-edge new research ideas that shapes the future of Technology?

We are seeking a driven and experienced Technical Manager to lead our clients research group. You will guide groundbreaking research projects in areas such as video and point cloud compression, AI/ML Applications & algorithm optimization for both performance and visual quality.

About the company:

MicroTECH Global are working with a company who are at the forefront of parallel data compression, with deep expertise in AI, data compression & XR (extended reality) They have developed multiple award-winning software products that are transforming industries such as TV, Live event production, Entertainment, Social Networks, Media, Aerospace, Automotive & Gaming

They achieve their goals by keeping our innovation cycle as agile and flexible as possible, with continuously testing and challenging assumptions. Thanks to the unique technology within the in-house testing facilities, they quickly pass from ideas to implementations.

Responsibilities:

As a Research Engineering Manager, you will:

• Leadership: Manage the day-to-day activities of the Research Group, ensuring timely project delivery, team performance, and recruitment of top talent.

• People Development: Actively mentor and develop engineers through regular 1:1s, objective setting, feedback, and addressing any concerns.

• Technical Leadership: Lead the design of algorithms and software for data compression systems, guiding your team in developing cutting-edge solutions.

• Documentation: Create and maintain technical documentation, including project reports, white papers, and intellectual property (IP) capture

• Process Improvement: Continuously enhance the Research Group’s processes and tools, driving efficiency and quality across the department.

Background and Experience:

• Passion for Research: A deep enthusiasm for advancing research in a dynamic environment.

• Pragmatism: A passion for hypothesis-driven innovation, enabling rapid cycles of iteration and fast-tracked delivery of Minimum Viable Products.

• Technical Expertise: Experience in designing and developing data compression solutions, AI/ML technologies, and/or C++ development.

• Leadership Skills: Proven ability to manage and mentor a skilled team, driving projects to completion within commercial deadlines.

• Communication Skills: Excellent written and verbal communication, including technical documentation and project reporting.

• Flexibility: Ability to thrive in an innovative, cross-functional environment where initiative and adaptability are key.

• Deep understanding of data compression technologies, including lossy/lossless compression, quality metrics, and colour spaces.

• Knowledge of the end-to-end software development lifecycle, with experience collaborating across teams.

• Proficiency in Python and C++ software development.

• Experience with parallel processing programming.

• Understanding of standardization processes and standard-developing organizations (SDOs).

Beneficial to have:

• Knowledge of objective Visual Quality (VQ) assessment techniques.

• Experience with TensorFlow, visual AI (e.g., media indexing), and/or multimodal Generative AI.

• Experience in Intellectual Property development.

• Experience presenting research findings at conferences and within video-centric forums.

• Contribution to standard-developing organizations (SDOs).

• Experience with Agile development methodologies and tools like JIRA.

• Proficiency in software development tools such as GIT.