Senior Machine Learning Engineer

At Monalee, we’re on a mission to accelerate the adoption of home solar, storage, and EV charging by making clean energy more accessible, transparent, and affordable. Founded in 2022, we’ve rapidly grown into a venture-backed climate tech company, leveraging advanced machine learning and AI to revolutionize the residential solar industry. Our proprietary platform, Artemis, enables instant, accurate solar proposals, streamlining the process for homeowners and installers alike. Our team is fully remote, highly skilled, and committed to delivering a high-quality user experience, while pushing soft costs low enough to make us one of the best values in solar. About the role We’re currently looking for Senior Machine Learning Engineers to help us scale our software suite artemis.solar What you'll do Design and implement deep learning models for 3D computer vision tasks, including object detection, segmentation, and depth estimation. Develop and maintain end-to-end machine learning pipelines encompassing data preprocessing, model training, evaluation, and deployment. Optimize models for real-time inference and deploy them using cloud platforms such as AWS SageMaker or GCP Vertex AI. Monitor deployed models, analyze performance metrics, and implement retraining strategies to ensure sustained accuracy and reliability. Document methodologies, experiments, and findings; actively participate in code reviews and technical discussions. Stay abreast of the latest research and advancements in machine learning and computer vision to inform model development. Qualifications Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; a master’s degree or relevant research experience is preferred. Minimum of four years of experience in developing and deploying machine learning models, with at least two years focused on computer vision applications. Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow; familiarity with models like DINOv2, ViTs, or SAM. Hands-on experience deploying ML models on cloud platforms (e.g., AWS, GCP) and building containerized services using Docker and Flask/FastAPI. Familiarity with data annotation tools and labeling strategies for supervised learning; understanding of data management best practices. Experience with geospatial data, including photogrammetry, LiDAR, or satellite imagery, is a plus. What we offer Professional development through courses, seminars, and certifications. Annual tech allowance. Health benefits. Stock options. Paid time off and vacations. Fully remote work. If you are passionate about machine learning projects to help accelerate renewable energies, looking for growth opportunities, and eager to face exciting challenges in a dynamic startup environment, we’d love to hear from you.