Hatch IT - Image & Computer Vision AI Engineer
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Requirements
• 3+ years of experience in computer vision, image processing, or applied machine learning. • Hands-on experience with computer vision models and techniques (e.g., CNNs, transformers for vision, feature embeddings). • Experience building or integrating image analytics such as facial recognition, object detection, or image similarity. • Strong programming skills in Python; experience with common CV/ML libraries (PyTorch, TensorFlow, OpenCV, etc.). • Solid understanding of machine learning fundamentals, model evaluation, and performance tradeoffs. • Experience working with large image datasets and production ML pipelines. • Ability to work collaboratively in a fast-moving, mission-driven engineering environment. • Experience with facial matching or biometric systems in regulated or high-stakes environments. • Experience with image-based geolocation or scene/location inference. • Familiarity with multimodal AI systems, including combining vision models with LLMs or natural-language search. • Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required. • Advanced degree is a plus but not required. • $140,000 - $170,000 a year • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Responsibilities
• Build and maintain computer vision pipelines for image ingestion, preprocessing, inference, and evaluation. • Implement facial matching, and identity-related vision workflows in accordance with accuracy, safety, and compliance requirements. • Develop and support object detection, image similarity, and scene understanding models. • Contribute to image-based geolocation and location inference capabilities using visual features and contextual signals. • Support multimodal AI workflows that combine image embeddings with LLM-based search and reasoning. • Write clean, maintainable Python code and contribute to production services and APIs. • Assist with model evaluation, bias testing, and accuracy monitoring for vision systems. • Optimize inference pipelines for performance, scalability, and cost efficiency (GPU usage, batching, model selection). • Collaborate with Product and Engineering teams to integrate vision capabilities into user-facing intelligence applications.
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