Note: The job is a remote job and is open to candidates in USA. Ursa Space Systems is a company that transforms complex satellite and spatial data into actionable insights. They are seeking an Applied AI/ML Engineer to design and maintain AI systems that automate geospatial analysis, work with various data sources, and ensure the reliability of AI outputs.
Responsibilities
- 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
- Do ad-hoc analysis and answer time-sensitive questions from stakeholders across the organization
- Act as a technical resource for teammates to bring awareness of new models, techniques, and tools that help the whole team grow
- Owning measuring actual observed error against budget, not just believing the budget on paper
- Adversarial and edge-case testing deliberately probing for failure modes (ambiguous imagery, conflicting sources, out-of-distribution inputs) rather than testing the happy path a developer already validated
- Confidence calibration to ensure that a system's stated confidence actually corresponds to real-world correctness rates, which is its own measurement discipline
- Experienced with traceability and reproducibility to demonstrate why the system produced a given output, on a specific input, at a specific model/data version
- All other duties as assigned
Skills
- 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
- Experience developing and deploying in AWS environments
- Strong communication skills: able to present findings and explain complex systems to technical and non-technical audiences, including customers
- 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
- Experience leading projects on complex, cross-discipline teams
- Familiarity with Confluence, Jira, and Miro
- Regression and drift detection: did a 'small' model or prompt change silently degrade accuracy on a case type that isn't in the dev team's day-to-day test set?
Benefits
- Discretionary PTO & Flexible Scheduling
- Stock Options
- 401(k) Match
- Medical, Dental and Vision Coverage for you and your dependents
- FSA & HSA Plans
- Employer-paid Life Insurance
- Employer-paid LTD and STD for Parental and Family Care
- 11 Paid Holidays
- Employee Resource Groups
- Educational Assistance Program
- Professional Development Opportunities
- And more…
Company Overview