Job Description
We’re looking for a Data Engineering Intern to support our Geospatial and AI Foundations work over a summer internship that transitions into part-time work through September. This role begins as a full-time, 3-month summer internship and then continues part-time through September.
This internship is designed for a graduate-level candidate who wants to bridge geospatial research, production data engineering, and applied AI systems.
You’ll contribute meaningful geospatial analyses, help build and improve production data pipelines, and experiment with AI-driven data access tools. This role plugs directly into the data and platform teams, supporting both research initiatives and operational data infrastructure. Your work will help transform complex geospatial insights into reliable, production-ready data assets used across the organization.
About your role at Ready ⚡️
You’ll split your time across three areas: geospatial data science, data engineering, and applied AI systems.
GeoData Science (Core Research Contribution)
Lead research-oriented analyses such as tree canopy classification, slope and terrain analysis, and spatial feature extraction at scale
Design and document reproducible analytical workflows
Translate complex geospatial methods into clear, accessible outputs for non-technical stakeholders
Connect research outputs to production data pipelines
Share learnings on emerging GeoAI methods with the team
Data Engineering (Skill Building with Mentorship)
Build or improve Airflow ELT pipelines with mentorship and clear documentation
Write clean, well-structured SQL and Python pipelines
Develop modular dbt models with semantic layer definitions and documented business logic
Contribute to data quality systems, including schema validation and freshness monitoring
Support DataHub adoption through schema documentation and lineage tracking
Communicate progress through documentation, code reviews, and updates
AI Engineering (Applied Learning)
Build data agents using tools like LangGraph, LangChain, or Bedrock Agent Core
Develop and maintain RAG pipelines for natural-language data access
Iterate on text-to-SQL approaches and document failure modes
Contribute to MCP server development as needed
Evaluate agent outputs and refine prompts based on feedback
A bit about you 🥇
Currently pursuing a Master’s or PhD in Computer Science, Data Science, GIS, Geospatial Engineering, or a related field
Available to work full-time for 3 months during the summer, then part-time through September
Strong fundamentals in SQL and Python
Familiarity with core geospatial concepts (CRS, spatial joins, indexing, spatial trees, optimization)
Familiarity with AI agent architectures (e.g., ReAct) and protocols (A2A, MCP, AG-UI)
Exposure to non-LLM geospatial deep learning approaches
Experience with Airflow or similar orchestration tools
Familiarity with GIS tooling such as PostGIS, GeoPandas, or QGIS
Interest in AI-assisted developer tooling
Ability to pair with senior engineers and participate in reviews and documentation
Strong communication skills to inform progress and blockers early
About Ready 🚀
Creative problem solvers approaching a legacy industry with a new point of view
Humble but ambitious, knowledgeable but curious, persistent but not obnoxious
Concise and effective in written and spoken communication
Comfortable working remotely
One team, one dream
About what you get…
Competitive hourly wage - $40-$45 per hour
100% remote work from home
Opportunities to learn and grow – all things startups
A chance to play a role in defining the roadmap as we pursue a bold vision and and a big goal
Work from (almost) anywhere. Ready is a remote-first company, but for security and compliance reasons, employees are not permitted to work from China (excluding Hong Kong, Macau, and Taiwan), Russia, Iran, or North Korea. These restrictions are in place to protect our systems, data, and intellectual property.
To get away - we all convene 1-2x a year for [optional, encouraged] retreats
The charter to realize a market that is set to receive $65 billion in grant funding across the United States
