Computer Vision & Machine Learning Engineer

Responsibilities

• Own computer vision projects from problem framing through production deployment and monitoring.
• Develop solutions for equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and intrusion surveillance.
• Translate client requirements and raw data into working computer vision solutions.
• Evaluate and implement methods from ML and computer vision research papers.
• Design experiments with hyperparameter tuning, ablation studies, baselines, and structured error analysis.
• Select and adapt model architectures based on latency and accuracy requirements.
• Build Python libraries, data pipelines, annotation workflows, model-serving pipelines, and quality-monitoring systems.
• Manage experiment tracking, dataset and model versioning, configurations, environments, and checkpoints.
• Conduct code reviews, write integration tests, and uphold software quality practices.
• Communicate research findings, technical decisions, and model limitations to teammates, stakeholders, and clients.

Requirements

• 2–5 years of industry experience in computer vision and machine learning.
• Strong knowledge of object detection, semantic segmentation, image classification, vision transformers, foundation models, vision-language models, and similarity search.
• Experience taking at least one machine learning model into production and maintaining it.
• Experience selecting, fine-tuning, and adapting CNN, transformer, and foundation-model architectures.
• Ability to read ML research papers, implement key ideas, debug training instabilities, and perform systematic error analysis.
• Proficiency in Python, PyTorch, Lightning, OpenCV, NumPy, pandas, Scikit-Learn, FastAPI, and Pydantic.
• Strong software engineering practices involving Git, Pytest, GitHub Actions, Docker, reproducible environments, experiment tracking, model versioning, ML DevOps, and Python type hinting.
• Ability to independently own technical projects from problem framing through production deployment.
• Preferred experience with multimodal computer vision, custom object detection, generative models for data augmentation, GIS or drone-metadata-enriched imagery, model quantization, edge latency optimization, and large-scale hyperparameter tuning.
• Experience in energy, utilities, geospatial, or industrial inspection domains is desirable.
• United States work-authorization sponsorship is not provided.