ML Fellowship: Weather Forecasting & Sensing - On-site

Lever, Inc.; El Segundo (CA); On-site; USD 81,000 - 97,000; Full time

Benefits offered by this job

Full health coverage (medical, dental,

Job summary

Rainmaker in El Segundo is offering a paid, full-time Machine Learning Fellowship for exceptional students and early-career researchers. You will join Rainmaker's R&D team and work on a scoped ML project with mentorship and collaboration across the team.

You will work with real sensor data, build and evaluate models, and deliver a final artifact such as a benchmark dataset or evaluated model. Rolling applications with flexible start dates and potential consideration for future roles.

Qualifications

• Strong Python programming ability.
• Experience with a modern ML framework.
• Evidence that you can independently build, test, and debug technical work.
• Strong quantitative reasoning and an ability to design credible experiments.
• Clear written and verbal communication.

Responsibilities

• Translate a scientific or operational question into a measurable ML problem.
• Build or improve the training and validation dataset for the project.
• Establish simple, reproducible baselines before more complex models.
• Train, evaluate, and debug models with held-out weather events or regions.
• Quantify calibration, uncertainty, and generalization.
• Produce clear, reusable code and documentation.
• Present results to Rainmaker's scientists and leadership.
• Deliver a final artifact such as a benchmark dataset or evaluated model.

Skills

Python

ML framework experience

Education

Undergraduate/Graduate student

Job description

Rainmaker in El Segundo is offering a paid, full-time Machine Learning Fellowship for exceptional students and early-career researchers. You will join Rainmaker's R&D team and work on a scoped ML project with mentorship and collaboration across the team.

You will work with real sensor data, build and evaluate models, and deliver a final artifact such as a benchmark dataset or evaluated model. Rolling applications with flexible start dates and potential consideration for future roles.