Spexi

Role Description We’re looking for a Senior Machine Learning Engineer to lead the development of experimental models, algorithms and prototype systems that push the boundaries of what’s possible with geospatial imagery analytics. Your work will bridge early-stage research and production—delivering high-quality, well-structured code that serves as a foundation for the next generation of Spexi’s geospatial intelligence products. - Design, train, and productionize models for aerial-image classification, object detection, and 2D/3D segmentation across diverse geographies, sensors, resolutions, and seasons. - Spearhead the integration and optimization of state-of-the-art foundation models for aerial segmentation, adapting SAM2-like capabilities and subsequent architectures through prompt engineering, fine-tuning, and distillation. - Engineer change detection and structure-change models capable of distinguishing real-world physical changes from acquisition noise and seasonal lighting variations. - Develop predictive models for trend forecasting, integrating time-series methods with spatial context to monitor vegetation growth, construction, and asset degradation. - Build Generative AI capabilities, including multimodal models and natural-language query systems that ground language in georeferenced pixels and semantic layers. - Design and operate scalable ML pipelines on AWS, leveraging SageMaker, S3, and Step Functions to move from research prototypes to production endpoints. - Track the frontier of research—including NeRFs, Gaussian Splatting, and diffusion models—translating relevant breakthroughs into shipped product capabilities. - Collaborate with photogrammetry and platform teams to ensure ML outputs maintain geospatial accuracy and align to coordinate reference systems. - Establish rigorous evaluation benchmarks and metrics to validate model performance under real-world production conditions. Qualifications - An M.S. and 5+ years of work experience in Computer Science, Computer Vision, Machine Learning, Remote Sensing, or a related quantitative field. - Proven experience in research-to-production translation: acting as the bridge between pure academia and commercial engineering. - At least 3 years of applied ML and computer vision experience transitioning models from research to production, ideally involving geospatial, aerial, or satellite imagery. - Deep, contemporary expertise in predictive AI for imagery, including classification, object detection, and segmentation. - Working knowledge of the SAM2 or similar algorithms—including fine-tuning, prompt design, and distillation. - Hands-on experience developing Generative AI capabilities, such as multimodal RAG, vision-language models, and diffusion-based pipelines. - Strong technical judgment regarding the selection of predictive vs. generative approaches. - Production experience operating ML pipelines on AWS, specifically utilizing SageMaker for training and hosting. - Experience managing large, complex imagery datasets in cloud environments. Requirements - A Ph.D. and 7+ years of work experience blending artificial intelligence with the physical sciences (e.g., Photogrammetry, Physics). - Deep expertise in geospatial and remote sensing workflows, including hands-on experience with georeferenced imagery. - Proven ability to adapt or pretrain geospatial foundation models to specialized remote-sensing tasks. - Specialized experience in 3D scene understanding, leveraging NeRFs, Gaussian Splatting, and point cloud segmentation. - Architectural experience designing multimodal RAG systems that integrate imagery, vector, and time-series data. - Background in fast-paced startup environments, with a demonstrated capability to translate experimental research into production-quality geospatial intelligence systems. Benefits - Remote-friendly environment (with a hub in Vancouver, Canada). - Flexible hours. - Medical, dental, and vision health benefits. - Spexi is an inclusive employer that values workplace equality, supports diversity, and respects the unique qualities each individual brings to the company.