Applied AI/ML Engineer - Ursa Space Systems

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. This is a hybrid role by design. Some weeks you'll be deep in computer vision training and deploying object detection and segmentation models on SAR and electro-optical satellite imagery. Other weeks you'll be building agentic systems: designing tool-calling workflows, orchestrating LLM-driven analysis pipelines, and building the evaluation infrastructure that keeps them reliable. The work varies significantly project to project, and the right candidate sees that as a feature, not a bug.

You'll report to the Director of Analytics and work side-by-side with data scientists, image scientists, product owners, and customers. This role may include pre-scheduled on-call rotations requiring occasional evening or weekend technical support.

This is a virtual role and an exempt position.

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
• Conduct Verification and Validation (V&V) against AI/ML and LLM outputs to ensure accurate resulting information
• Train, fine-tune, evaluate, and deploy computer vision models (object detection, segmentation, change detection) on SAR, EO, and other Earth Observation data
• Adapt and fine-tune vision-language models (VLMs) to move beyond bounding boxes toward full scene understanding and extracting the meaningful content of imagery, not just locating objects
• Build evaluation and observability infrastructure for both classical ML (precision/recall, IoU, AUC/ROC) and LLM/agent behavior (task success, groundedness, regression testing), and use it to drive measurable improvement
• Own projects end to end: from data definition and prototyping through production deployment, validation, and maintenance
• Work directly with product owners, internal platform users, and external customers to understand needs, translate requirements into agentic system designs, and explain how the technology works to people who experience it as a black box
• Integrate third-party and multi-source data sets into analysis pipelines