Computer Vision Algorithms Engineer - Edge AI

About the role Trego is looking for an experienced Computer Vision Algorithms Engineer to take ownership of computer vision models for smart occupancy-sensing applications. You will design, train, evaluate, optimize, and deploy models for privacy-preserving occupancy sensors. This is a hands-on role combining applied research with production engineering, with a strong focus on reliable performance on resource-constrained edge devices. What you will do Develop and maintain models for people detection, counting, and tracking, combining deep learning with classical computer vision where appropriate. Build reproducible data, training, evaluation, and model-release pipelines. Define datasets, annotation requirements, and evaluation protocols that reflect real deployment conditions. Analyze field data and model failures to improve accuracy and overall product performance. Optimize and deploy models within accuracy, latency, memory, compute, and power constraints. Collaborate with embedded software, hardware, QA, product, and customer-facing teams on integration, validation, and issue investigation. What you bring At least 5 years of industry experience in computer vision or deep learning, including object detection and tracking. Strong proficiency in Python and C++, together with NumPy, OpenCV, and PyTorch or TensorFlow. Demonstrated experience taking computer vision models from research into production. Hands-on experience optimizing neural networks for constrained edge hardware, including INT8 quantization and performance profiling. A strong understanding of model evaluation, dataset quality, overfitting, distribution shift, and failure analysis. An M.Sc. in Computer Science or Electrical Engineering, with a specialization in computer vision. The ability to work independently and communicate clearly with technical and nontechnical stakeholders. Professional working proficiency in English. Additional advantages Experience with YOLO models and model-conversion tools such as ONNX or TensorFlow Lite. Experience with embedded Linux, camera pipelines, real-time systems, or NPU and DSP toolchains. Experience with low-resolution or wide-angle imagery, people counting, or occupancy analytics. Experience with MLOps. Experience using AI-assisted development tools.