Applied AI/ML Engineer

About The Role

Ursa Space turns complex satellite and spatial data into decision-ready answers. Our agentic GeoAI platform lets users ask a question in plain English about a place, an event, or an activity anywhere on Earth and handles the rest: selecting the right data sources, tasking sensors, running analytics, and delivering an insight report in minutes instead of hours. We're looking for an Applied AI/ML Engineer to help build the intelligence behind that platform.

Job Summary

• Design, build, and maintain agentic AI systems that automate stages of the geospatial analysis cycle from natural-language question intake through data selection, multi-source analysis, and report generation
• Develop tool-use, orchestration, and context-management capabilities that connect LLMs to our geospatial data services, analytics, and 90+ integrated data feeds
• Evaluate ML systems with appropriate metrics — both traditional (RMSE, FPR/TPR, AUC/ROC, IoU) and LLM/agent evaluation approaches
• Own projects end to end: from data definition and prototyping through production deployment, validation, and maintenance
• Integrate third-party and multi-source data sets into analysis pipelines
• Act as a technical resource for teammates to bring awareness of new models, techniques, and tools that help the whole team grow
• Lead projects on complex, cross-discipline teams

Requirements

• B.S. or M.S. in Computer Science, Data Science, or a related STEM field
• 4–6 years of experience building and deploying machine learning systems for product- or software-focused organizations
• Strong Python and production software engineering practices: Git, Docker, testing, code review, CI/CD
• Experience training and deploying deep learning models for computer vision tasks (object detection, image segmentation) using PyTorch or similar frameworks
• Hands-on experience building LLM-powered applications: prompt design, structured outputs, tool use / function calling, and agentic architectures
• Experience evaluating ML systems with appropriate metrics — both traditional (RMSE, FPR/TPR, AUC/ROC, IoU) and LLM/agent evaluation approaches
• Strong communication skills: able to present findings and explain complex systems to technical and non-technical audiences, including customers

Preferred Skills

• Prior experience with remote sensing data especially synthetic aperture radar (SAR), GIS, and/or spatial statistics
• Experience fine-tuning foundation models or VLMs (e.g., LoRA/PEFT, multimodal adaptation for domain-specific imagery)
• Familiarity with agent orchestration frameworks and protocols (e.g., LangGraph, Claude Agent SDK, MCP) and LLM observability/eval tooling
• Geospatial Python stack: GDAL, rasterio, geopandas, xarray
• Experience with SQL/NoSQL databases and vector stores