Machine Learning Engineer

Company Description MineralAI LLC is transforming geological exploration and autonomous navigation through single-photo 3D reconstruction technology that converts smartphone images into detailed 3D models and mineral classifications in under 60 seconds. The AI-powered platform delivers 96% mineral classification accuracy, is 10x faster than traditional photogrammetry, and operates with a 0.9% error rate, outperforming industry standards. The technology stack includes large-scale ML models, ResNet50-based computer vision, an 8B-parameter LLM with RAG, and a security-first design featuring end-to-end encryption and no data storage. MineralAI targets high-growth markets such as geological exploration, aerial surveying, smart logistics, and autonomous vehicles, with a strong focus on Ukraine’s significant untapped resources and upcoming post-war reconstruction opportunities.Role Description This is a full-time, on-site Machine Learning Engineer role based in Zhytomyr. The Machine Learning Engineer will design, develop, and optimize machine learning models for single-photo 3D reconstruction, mineral classification, and autonomous navigation use cases. Daily responsibilities include experimenting with neural network architectures, implementing and improving algorithms for pattern recognition, analyzing statistical performance metrics, and deploying models into production systems. The role involves close collaboration with data scientists, software engineers, and domain experts to refine datasets, enhance model robustness, and ensure scalability and security of the ML pipeline. The Engineer will also contribute to system monitoring, performance tuning, documentation, and continuous improvement of the overall AI platform.Qualifications Strong foundation in Computer Science and Algorithms, with the ability to design efficient, scalable solutions.Applied expertise in Neural Networks and Pattern Recognition for computer vision and 3D reconstruction tasks.Solid knowledge of Statistics for model evaluation, experimentation, and performance analysis.Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow) and Python-based data science tooling.Experience building and deploying production-grade ML systems, including model optimization and inference pipelines.Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related quantitative field.Familiarity with cloud or edge deployment environments, API development, and secure data handling practices.Ability to work collaboratively on-site in Zhytomyr, communicate clearly with cross-functional teams, and adapt to a fast-paced environment.Background in computer vision, 3D geometry, or geospatial data, and experience in high-growth or deep-tech startups is a plus.