Research Engineer

Are you interested in transforming climate tech?

Do you thrive in a fast-moving R&D environment with state-of-the-art technologies?

Would you like to build advanced AI models for petabyte-scale systems?

We're working with an innovative open-source climate data start-up focused on building transformative ML models to predict weather and global climate challenges. The team brings a depth of world-class experience from Google X, MIT, and Descartes Labs. They plan to democratize access to powerful, open-source climate information, enabling researchers, government agencies, academics, and other groups to leverage vast datasets for predictive modeling on a petabyte scale.

Role Responsibilities:

• Conduct extensive ML training using PyTorch, with a strong focus on statistical, data-driven tasks.
• Engage in experimental R&D, contributing to idea generation and analytical problem-solving.
• Experiment with and potentially explore ML, Graph Neural Networks (GNNs), transformers, and diffusion models.
• Deploy ML models using containers and Docker.
• Build and optimize data loaders for efficient data processing.

Qualifications:

• Strong experience with PyTorch or other ML frameworks.
• Solid background in statistical analysis and data-driven research.
• Added bonus: Familiarity with developing and experimenting with GNNs, transformers, and diffusion models.
• High-level research/industry innovation experience in the ML space.

Preferred Qualifications:

• Ph.D. in Machine Learning, Data Science, Climate Science, or a related field.
• Experience working with large datasets, particularly in climate science or related domains.
• Background in working with open-source projects and contributing to the community.

Please note, this company is not yet considering sponsorship of visas (e.g. H-1B). US citizens and permanent residents will be prioritized, at least a the start of this search.

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