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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Rainmaker Fellow, Machine Learning - **Company:** Rainmaker Technology Corporation - **Location:** El Segundo, CA, United States - **Salary:** $96,000.0 - **Contract:** Temporary contract - **Skills:** Computer Vision, Software Debugging, Python (Programming Language), Machine Learning, Operational Data Store, Scientific Computating, Sensor Fusion, Data Assimilation, Information Technology - **Published:** July 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cd4f877a67901d29 ## About the Role * Current undergraduate, master's, or PhD students; postdoctoral researchers; recent graduates; and other early-career researchers are all eligible. * Strong Python programming ability and 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. * Interest in noisy, sparse, multimodal, spatial, temporal, or physical data. * Ability to make progress on ambiguous research problems while incorporating mentor feedback. * Clear written and verbal communication. * Availability for full-time, on-site work in El Segundo for the agreed appointment., * Machine learning, computer science, applied mathematics, statistics, physics, meteorology, remote sensing, robotics, autonomy, geospatial analysis, or scientific computing. * Forecasting, sequence modeling, computer vision, state estimation, sensor fusion, probabilistic modeling, data assimilation, or uncertainty quantification. * Weather knowledge is valuable but not required. ## Description The Rainmaker Machine Learning Fellowship is a paid, full-time research appointment for exceptional undergraduate and graduate students, postdoctoral researchers, recent graduates, and other early-career researchers. As a fellow, you will join Rainmaker's R&D team and work alongside our researchers on a scoped machine-learning project drawn from Rainmaker's current research priorities and defined in close collaboration with your research lead or mentor. Project matching will consider available data, mentor capacity, team needs, and your background. You will take responsibility for a concrete workstream while contributing to the broader team's research, reviews, and technical decisions. You will work with real sensor and operational data, establish credible baselines, build and evaluate models, and leave behind a durable dataset, system, or research artifact that Rainmaker can continue using. Fellows are not expected to arrive with an independent research agenda or define a project in isolation., Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our ML team may pursue include: * Developing a short-range supercooled liquid water opportunity forecast using public NWP and Rainmaker observations. * Predicting hail-core growth, motion, splitting, and decay from radar sequences. * Building a bounded multimodal atmospheric-state reconstruction pilot. * Improving microwave-sounder retrievals using Rainmaker observations. * Modeling another scientific or operational problem selected with Rainmaker's ML and atmospheric-science teams., * Translate a scientific or operational question into a measurable ML problem. * Build or improve the training and validation dataset needed for the project. * Establish simple, reproducible baselines before introducing more complex models. * Train, evaluate, and debug models using held-out weather events, regions, or operating conditions. * Quantify calibration, uncertainty, generalization, failure modes, and sensitivity to missing or biased data. * Work closely with atmospheric scientists to define useful targets, ground truth, physical constraints, and operational success criteria. * Produce clear, reusable code and documentation. * Present your results to Rainmaker's scientists, engineers, operators, and technical leadership. * Deliver a final artifact such as a benchmark dataset, model, prototype product, evaluation report, or research paper. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [Focoos AI: Building the Future of Computer Vision](https://www.wearedevelopers.com/videos/1659-focoos-ai-building-the-future-of-computer-vision) - [Robots 2.0: When artificial intelligence meets steel](https://www.wearedevelopers.com/videos/1452-robots-2-0-when-artificial-intelligence-meets-steel) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [6 Emerging Technologies We’ll Learn About in 2025](https://www.wearedevelopers.com/magazine/381-6-emerging-technologies-we-ll-learn-about-in-2025) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)