Terrafuse is building AI technology that makes climate and weather modeling 1000x faster and 100x cheaper than any other product on the market, unlocking fundamentally new use cases in climate risk and other business domains.

Our technology combines our academic expertise in climate science and machine learning, developed from our work at Lawrence Berkeley National Lab and supported by the National Science Foundation, NASA the U.S. Department of Defense, and Microsoft AI for Earth. Our vision is to develop a Queryable Earth in which cloud-native Earth observation and climate data are combined with AI to produce actionable insights into climate for businesses and society.

Our starting points are wildfire and wind. Extreme wildfire conditions have increased by 50% in the last few decades as a result of climate change and insurance companies are now refusing to insure homes and businesses in California, because they are unable to accurately predict their losses. Our product enables accurate pricing of this insurance, making it again economically possible to offer coverage to homeowners and businesses. Our customers also include government players in aviation and clean energy.

Terrafuse has developed a groundbreaking wildfire risk model that accurately predicted the devastating Los Angeles fires and continues to anticipate future fire events. This technology plays a critical role in disaster preparedness and response, and the work of this intern could directly contribute to saving lives and mitigating environmental damage.

Role Overview: We are seeking a highly skilled and motivated Environmental Engineer, Intern, to join our team. This role is ideal for someone who has strong technical expertise in environmental engineering, data analysis, and geographic information systems (GIS). As an intern, you will support critical projects by utilizing Python and ArcGIS to analyze and interpret environmental data, contributing to impactful solutions for our clients.

Location: Remote or San Francisco Bay Area

Status: Part-Time Internship

Key Responsibilities:

Validate machine learning based catastrophe risk models and assess their alignment with environmental and client standards.
Analyze and process environmental datasets using Python and ArcGIS.
Assist in creating visualizations and maps for risk models.
Contribute to reporting and presentations by providing clear and actionable insights from data analysis.

Qualifications:

5-7 years of experience in software engineering, environmental engineering, data science, or a related field, with a focus on GIS and data analysis (cumulative academic and professional experience acceptable).
Proficiency in Python for data analysis, visualization, and modeling. Knowledge of other programming languages is a plus.
Strong knowledge of ArcGIS or QGIS, including spatial analysis, data management, and mapping.
Familiarity with validating catastrophe risk models and understanding their implications.
Excellent problem-solving skills and attention to detail.
Effective communication and teamwork abilities.

Preferred Qualifications:

Experience building machine learning models.
Experience with cloud-based GIS solutions and data platforms.
Knowledge of machine learning or statistical modeling for environmental applications.
Familiarity with additional programming languages or tools, such as R or QGIS.
Undergraduate degree in technical discipline, such as data science, computer science, environmental engineering, or electrical engineering.

What We Offer:

Flexible part-time hours to accommodate your schedule.
Hands-on experience working on impactful environmental projects.
Opportunity to collaborate with a team of passionate professionals in a dynamic environment.
Mentorship and professional development opportunities to advance your career.

Application Process: To apply, please submit your resume, a cover letter highlighting your relevant experience, and a portfolio or examples of previous work. Applications will be reviewed on a rolling basis.

Terrafuse AI is committed to creating an inclusive and diverse workplace. We encourage candidates of all backgrounds to apply and join us in our mission to drive sustainable change.

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