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.

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.

Senior Machine Learning Engineer

Key skills