At a Glance
- Tasks: Design and develop advanced computer vision systems for the construction industry.
- Company: Join Depixen, a leading tech company transforming construction with AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement opportunities.
- Why this job: Make a real impact by connecting visual AI with verified data in a complex industry.
- Qualifications: Bachelor's degree in a relevant field and 3-6 years of experience in computer vision.
The predicted salary is between 60000 - 80000 € per year.
About the Role
This role is based at Depixen’s London Office. Depixen is a London-based technology company building the digital decision infrastructure of the construction industry. As a corporate member of the World Wide Web Consortium (W3C), Depixen develops W3C-compliant Linked Data architectures, domain-specific ontologies, taxonomy models, RDF-based data structures, and knowledge graph infrastructures for the construction sector. Through its AI projects with respected universities and institutions in the United Kingdom, Depixen continues to build a scalable global structure across the United States, Europe, and the Far East.
Computer vision is one of the critical perception layers of this infrastructure. In construction, architecture, and building products, visual data is not merely unstructured; it is deeply contextual. Product images, technical documents, drawings, site photos, spatial data, material surfaces, and building elements must be interpreted together with verified technical knowledge, semantic classifications, and machine-interpretable data models. This is not a conventional image recognition role. You will help connect visual AI outputs with verified data, taxonomy, ontology, RDF, and knowledge graph layers, turning perception into reliable decision intelligence for the construction industry.
We are seeking a talented Computer Vision Engineer to design, develop, and productionize advanced perception systems for real-world construction industry use cases. In this role, you will work on object detection, segmentation, OCR, visual matching, product recognition, building element analysis, site image interpretation, video analytics, and multimodal vision-language applications. The ideal candidate is analytical, research-driven, collaborative, and capable of turning advanced computer vision ideas into reliable production systems that address real industry problems.
Responsibilities
- Design, develop, and evaluate robust, scalable, and verifiable computer vision models and pipelines using modern deep learning frameworks.
- Build and optimize end-to-end vision systems covering data preprocessing, model development, deployment, monitoring, and continuous improvement.
- Develop systems for object detection, segmentation, tracking, OCR, image classification, visual matching, product recognition, and video analytics as required.
- Collaborate with data and modelling teams to connect visual AI outputs with taxonomy, ontology, RDF, and knowledge graph layers.
- Work with cross-functional teams to understand product requirements and translate them into scalable technical solutions.
- Develop automated testing, benchmarking, and evaluation workflows to ensure the performance, reliability, and safety of computer vision applications.
- Optimize models and inference pipelines for scalability, latency, and cost-efficiency across GPU and CPU environments.
- Contribute to dataset design, annotation processes, data quality validation, and tools that improve model performance and reliability.
- Translate research-level computer vision and multimodal AI approaches into production-ready technical solutions.
Required Qualifications
- Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, Artificial Intelligence Engineering, or a related field.
- 3-6 years of experience in computer vision, deep learning, or a related AI field.
- Strong proficiency in Python.
- Hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, or similar.
- Practical experience developing, testing, and deploying computer vision models in production environments.
- Solid technical understanding of convolutional and transformer-based architectures, such as CNNs, ViT, YOLO, and Detectron2.
- Hands-on experience in several of the following areas: object detection, segmentation, OCR, tracking, image classification, or video analytics.
- Experience with ML Ops practices and tools such as Docker, Kubernetes, MLflow, and Weights & Biases.
- Systematic approach to model evaluation, benchmarking, data quality control, and error analysis.
- Familiarity with GPU/CPU inference optimization, latency management, and model deployment workflows.
- Ability to analyse technical problems clearly, document solutions effectively, and communicate across teams.
Preferred Qualifications
- Master’s or PhD degree in a relevant field.
- Experience implementing or fine-tuning vision-language models such as CLIP, BLIP, or SAM.
- Experience with multimodal AI, visual grounding, open-vocabulary detection, or image-text retrieval.
- Familiarity with edge deployment frameworks such as TensorRT, OpenVINO, or ONNX Runtime.
- Experience with 3D vision, point clouds, depth estimation, SLAM, spatial intelligence, or digital twin-based visual analysis.
- Experience working with construction, architecture, construction technologies, BIM, technical document analysis, or product data systems.
- Experience with OCR, technical document processing, drawing analysis, catalogue data extraction, or visual product matching.
- Contributions to open-source computer vision projects.
- Experience deploying models on cloud platforms such as AWS, GCP, or Azure.
- Experience with data annotation, synthetic data, active learning, or dataset quality management.
Problem Areas You May Work On
- Visual recognition and classification of building products.
- Matching product images with technical data, catalogue information, and semantic classifications.
- OCR-based data extraction from technical documents, catalogues, and PDFs.
- Joint analysis of architectural drawings, site photos, and product images.
- Detection of building elements and material surfaces.
- Linking visual data with taxonomy, ontology, and knowledge graph structures.
- Applying vision-language models in the construction industry context.
- Quality, compliance, or contextual analysis from site imagery.
- Connecting visual AI outputs to verified, structured, and machine-interpretable data infrastructure.
Why This Role Different
The construction industry is one of the most complex application areas for computer vision because it brings together architecture, engineering, construction, building materials, and site operations. In this domain, the meaning of an image cannot be derived from pixels alone. The product class, technical standard, usage context, relationship to building elements, material characteristics, performance values, and verifiable data counterpart must be considered together. At Depixen, computer vision outputs are not treated as isolated predictions. They are treated as decision components connected to verified data, semantic classification, ontology, RDF, and knowledge graph layers. This makes the role not only about model development, but about building reliable, contextual, and verifiable AI systems for the construction industry.
AI Engineer [Computer Vision] in London employer: Depixen
Depixen is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets the construction industry. With a strong focus on employee growth and collaboration, team members are encouraged to engage in cutting-edge AI projects that directly impact real-world applications. The company fosters a culture of continuous learning and development, providing unique opportunities to work alongside leading universities and institutions while contributing to meaningful advancements in technology.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer [Computer Vision] in London
✨Tip Number 1
Network like a pro! Attend industry meetups, conferences, or online webinars related to AI and computer vision. Engaging with professionals in the field can open doors and give you insights into job opportunities that might not be advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to computer vision and deep learning. Use platforms like GitHub to share your code and demonstrate your expertise in building models and pipelines.
✨Tip Number 3
Tailor your approach! When reaching out to potential employers, mention specific projects or technologies they use that excite you. This shows you've done your homework and are genuinely interested in their work, making you stand out from the crowd.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities at StudySmarter, and applying directly can give you a better chance of getting noticed. Plus, it’s super easy to do!
We think you need these skills to ace AI Engineer [Computer Vision] in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Engineer role. Highlight your experience with computer vision, deep learning frameworks, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about computer vision and how you can contribute to our mission at Depixen. Be sure to mention specific experiences that relate to the job description.
Showcase Your Projects:If you've worked on any cool projects related to computer vision or AI, make sure to include them in your application. We love seeing practical examples of your work, especially if they demonstrate your problem-solving skills in real-world scenarios.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Depixen
✨Know Your Tech Inside Out
Make sure you’re well-versed in the deep learning frameworks mentioned in the job description, like PyTorch and TensorFlow. Brush up on your knowledge of convolutional and transformer-based architectures, as these will likely come up during technical discussions.
✨Showcase Real-World Applications
Prepare to discuss specific projects where you've developed or deployed computer vision models. Highlight how you tackled challenges in object detection, segmentation, or OCR, and be ready to explain the impact of your work in a real-world context, especially in construction or related fields.
✨Collaborate and Communicate
Since this role involves working with cross-functional teams, practice articulating your ideas clearly. Be prepared to discuss how you’ve collaborated with others to translate product requirements into technical solutions, and demonstrate your ability to communicate complex concepts effectively.
✨Understand the Industry Context
Familiarise yourself with the construction industry and its unique challenges. Be ready to discuss how computer vision can solve specific problems in this sector, such as linking visual data with taxonomy and ontology, and why that’s important for decision-making in construction.
