Solo Tech empowers people and organizations to run powerful, privacy-first AI entirely on their own devices. We’re looking for talented students to push the boundaries of on-device intelligence across a variety of real-world use cases. From hyper-personalized recommendations to predictive lifetime-value models, fraud detection, and advanced classification tasks—all while keeping data secure and offline.

This will involve several different steps for the students, including:

1. Explore the Domain – Dive into Solo Tech’s product ecosystem, privacy principles, and the structure of the provided dataset.
2. Data Analysis – Profile, clean, and augment the data to surface patterns suited to on-device learning.
3. Technique Scouting – Survey the latest AI/ML advances (e.g., quantized transformer variants, federated learning, anomaly-aware RAG pipelines) and map them to Solo Tech’s hardware-aware stack.
4. Model Development – Build and fine-tune at least one on-device model that produces novel insights or high-value predictions.

By the end of the project, examples of outcomes are:

1. Technical report detailing research, methodology, and evaluation metrics.
2. Executable prototypes packaged for Solo Tech’s CLI or container runtime.
3. Deployment guide outlining how each model can be integrated into Solo Tech Studio (desktop), Solo App (tablet/mobile), and Solo Extension (hardware integrations).