HighTide Intelligence Internship
Positions: Data Science/Software Engineering Intern
Location: Bay Area
Innovation Area: Energy, Food, Water, Human Health and Environment Nexus
Position Type: TomKat Center-supported
Internship Term: 8 weeks
HighTide is building a platform to make the world resilient to the worst effects of climate change. Our product is a cloud-based platform that allows lenders, investors, and reinsurers to future-proof their portfolios against climate-driven flooding, while also informing the public of their exposure to sea-level rise. We’re using AI and big data to quantify flooding risks to all properties around the country and determine what makes certain places more vulnerable to climate impacts than others.
Our team is currently composed of graduates from Stanford’s Geophysics and Environmental Engineering departments who are passionate about sustainability, urban resilience, and climate adaptation. We grew out of a service learning course at Stanford and have been awarded prestigious grants from Stanford’s TomKat Center and Microsoft’s AI for Earth program.
Data Science/Software Engineering Intern
As a data science/software engineering intern, you will be working with our technical team on fully building out our product and data stack. Potential projects would include working on back-end cloud architecture, integration of new geospatial data including flood maps, and deploying machine learning and sensitivity analysis in our product.
Who Should Apply
We’re looking for talented, diverse, and motivated students eager to work on pressing, real world problems. You should be able to learn quickly on the job, be willing to take on new challenges as they come and get things done, and share our vision for creating a more resilient future. Additionally, you should be comfortable working on open-ended projects and pivoting focus as needed.
- Background in science/engineering, through field of study or work experience
- Experience working with Python, Django, or GIS
- Knowledge of or interest in cloud computing infrastructure
Experience with either geospatial modeling, civil engineering, geostatistics, or machine learning is preferred but not essential. Preference given to physical & earth science, engineering, and CS majors; however, we are open to anybody with the appropriate skills and interests who shares our vision.