- Make Rain
- El Segundo, BC
- Full-Time
- 65 days ago
- USD 160,000 – USD 220,000
Machine Learning Researcher.
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Machine Learning Researcher: our view in 3 lines...
- The Role:This role is for a machine learning researcher working on scientific and operational machine learning applications in weather and climate-related domains.
- The Person:The person will assess ML projects, build models and prototypes, create datasets and evaluation methods, and work with scientists and engineers to turn research into useful systems.
- Requirements:The ideal candidate has strong command of modern machine-learning methods, strong Python skills, and experience with PyTorch, JAX, statistics, forecasting, and data-constrained scientific domains.
About the role
About Rainmaker
Rainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.
Research at Rainmaker is attached directly to operations. Our scientists and engineers collect proprietary observations, deliberately intervene in atmospheric systems, evaluate the results, and use what they learn to improve the next operation.
About the Role
Rainmaker is hiring its first dedicated Machine Learning Researcher. You will establish how Rainmaker uses machine learning across the company: identifying the most valuable problems, determining which are ready for ML, building working models, and partnering with engineers and domain experts to turn successful research into operational systems.
You will not inherit a single predetermined model roadmap. The opportunity set includes forecasting supercooled liquid water and cloud-seeding opportunities, assimilating multimodal observations into estimates of atmospheric state, improving microwave-sounder retrievals, predicting hail, learning from intervention outcomes, and finding other high-leverage applications across research and operations.
Rainmaker's long-term advantage is not a generic weather model. It is the combination of proprietary in-cloud observations, radar and satellite data, UAS measurements, field campaigns, and repeated atmospheric interventions. You will build the learning systems that turn those data into better estimates, predictions, and decisions.
This is initially a hands-on individual-contributor role. You may eventually help recruit or technically lead an ML team if that fits your strengths and Rainmaker's needs, but management is not an initial expectation.
