computer vision / ML engineer

Mad Devs works on complex real-world engineering challenges and applied AI projects involving engineering data, production constraints, and customer requirements.

• Design and develop computer vision and machine learning solutions for architectural drawings, technical documents, CAD, and BIM data
• Build pipelines combining raster images, vector geometry, structured engineering data, OCR, and ML models
• Reconstruct structured objects from geometric primitives, including topology and spatial relationships
• Analyze dense technical drawings and documents based on their structure and semantics, not only visual object detection
• Design and maintain evaluation pipelines with ground-truth data, quality metrics, regression testing, and validation on unseen data
• Build human-in-the-loop workflows with calibrated confidence scores and escalation of uncertain cases for human review
• Evaluate modern approaches to floor-plan and technical-document understanding, including detection, segmentation, transformer-based models, and raster-to-vector reconstruction
• Develop production-ready inference services with CPU inference by default and optional GPU acceleration
• Make training and inference reproducible, including model versioning, packaging, and deployment requirements
• Transfer models to customers for independent deployment and further training
• Provide technical leadership on ML and computer vision decisions within a small engineering team
• Communicate directly with technical stakeholders and explain ML limitations, trade-offs, and decisions in English
• Challenge proposed approaches when needed and recommend better alternatives based on technical evidence

• 5+ Years of professional experience in machine learning or computer vision, including taking ML solutions into production
• Strong knowledge of computer vision, machine learning, and model evaluation
• Proven experience validating model quality using measurable metrics and real-world data
• Practical experience with vector geometry, structured data, topology, and spatial relationships
• Experience processing technical documents, diagrams, or drawings where structure and relationships matter
• Experience designing evaluation pipelines, including ground-truth datasets, regression testing, and generalization testing
• Experience building human-in-the-loop ML systems and handling uncertain predictions
• Experience making ML training reproducible and preparing models for independent deployment or further training
• Understanding of model and dataset versioning and relevant licensing considerations
• Experience providing technical leadership within a small engineering team
• Experience communicating directly with technical customers or stakeholders
• English at B2–C1 level with strong spoken communication skills
• Russian at C1–C2 level or native proficiency
• Будет плюсом: experience with architectural or construction drawings, BIM models, quantity takeoff, or cost estimation; familiarity with research in floor-plan recognition, raster-to-vector reconstruction, or structured document understanding; experience parsing CAD and BIM formats, including malformed, incomplete, or inconsistent exports; experience with OCR for European languages and industry-specific terminology; experience optimizing ML inference for CPU-only environments; experience deploying ML services in isolated customer environments without external dependencies; experience with graph-based representations of engineering structures or building layouts; ability to read technical drawings in German or French; German or French at B1–B2 level

• Flexible working hours
• Long-term projects
• Salary in dollars
• Professional communities
• Onsite business trips
• Training budget
• Paid conferences