Senior Computer Vision & Machine Learning Engineer

What you'll do

• Own end-to-end computer vision projects, from problem framing through deployment and monitoring.
• Evaluate and implement research methods, run experiments, and analyze model errors and performance.
• Build production ML libraries, data pipelines, and model-serving systems; contribute to code reviews and team practices.

What they ask for

• 5–10 years of industry experience in computer vision and machine learning.
• Expertise in modern computer vision and deep neural networks, including object detection, segmentation, classification, vision transformers and foundation models, vision language models, and similarity search.
• Track record deploying and maintaining production ML models; ability to adapt architectures, implement research papers, and debug training issues.
• Proficiency in Python and the stated ML and software engineering stack, including testing, CI/CD, Docker, experiment tracking, and ML DevOps.

Nice to have

• Multimodal computer vision, custom object detection, generative models for data augmentation, or edge-device deployment.
• GIS or drone metadata-enriched imagery, model quantization, or systematic hyperparameter tuning.

Tools

Python, PyTorch, Lightning, OpenCV, NumPy, pandas, Scikit-Learn, FastAPI

AI focus

Develop computer vision and machine learning models for equipment defect detection, thermal anomalies, vegetation encroachment, and human or animal intrusion; apply deep learning and generative AI advances to model training, accuracy, and reliability.