The Company

We are excited to partner with a mission-driven climate adaptation company, focused on helping the world through this major climate transition. They bring the latest, actionable climate intelligence information from the research domain to the real world, to build resilience into businesses and governments in the face of a warming planet. They are motivated by purpose – to improve the state of the planet while building an amazing company. They love science and technology, and believe that they can be harnessed for the global good. If you'd like to join this early team as a foundational team member, keep reading!

Your Impact (Responsibilities)

This pioneering role offers the unique opportunity to take ownership and collaborate closely with our Climate and AI scientists from the ground up, bringing innovative climate forecasting technology directly to our customers. As the first ML engineer on our team, you will play a crucial role in shaping the future of climate technology.

• You are responsible for spearheading the development and implementation of ML models, taking full ownership of the process
• You will work hand-in-hand with Climate and AI scientists, you will translate cutting-edge research into scalable solutions
• You are excited to tackle complex problems with innovative approaches, always looking for the most effective and efficient solutions
• You embrace a growth mindset in a fast-paced startup environment, where learning and adapting are part of the daily routine as you are continuously expanding your knowledge and skills in both machine learning and climate science
• You have a proven record of implementing ML models from scratch, utilizing your deep understanding of machine learning principles and algorithms
• You are creative as you design and test new models, always thinking outside the box to overcome challenges and improve forecasting accuracy

About You

• 1-3 years of experience in an ML engineer role, demonstrating a track record of successful ML model development and implementation
• Expertise in Python, including experience with ML libraries and frameworks such as PyTorch and TensorFlow
• Proficient in data wrangling and analysis, with the ability to clean, manage, and interpret large datasets effectively
• Solid understanding of machine learning principles, algorithms, and practices. Ability to implement, train, and validate models from scratch
• Strong software development practices, including version control (Git), continuous integration, and coding standards

‍Preferred:

• Experience with spatiotemporal data analysis and modeling, particularly in the context of climate or environmental data
• Proficiency in using advanced data analysis libraries such as xarray for multidimensional arrays, particularly for handling netCDF data formats
• Familiarity with geospatial data processing tools and libraries (e.g., GDAL, QGIS) and understanding of geospatial data formats and standards