Dhruv Diddi
Founder
Companies
  • Solo Tech
    Mountain View, California, United States
Categories
Robotics Software development Machine learning Artificial intelligence

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Solo Tech
Solo Tech
Mountain View, California, United States

Solo Tech Physical AI Fine Tuning

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: Explore the Domain – Dive into Solo Tech’s product ecosystem, privacy principles, and the structure of the provided dataset. Data Analysis – Profile, clean, and augment the data to surface patterns suited to on-device learning. 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. Model Development – Build and fine-tune at least one on-device model that produces novel insights or high-value predictions.

Matches 0
Category Data analysis + 4
Open
Solo Tech
Solo Tech
Mountain View, California, United States

Solo Tech Physical AI Fine Tuning

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: Explore the Domain – Dive into Solo Tech’s product ecosystem, privacy principles, and the structure of the provided dataset. Data Analysis – Profile, clean, and augment the data to surface patterns suited to on-device learning. 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. Model Development – Build and fine-tune at least one on-device model that produces novel insights or high-value predictions.

Matches 0
Category Data analysis + 4
Closed