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Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis:2. Build a generic ML framework which leverages relationship between places to improve delivery experience by learning precise delivery locations and propagating attributes, such as business hours and safe places. This requires us to combine a variety of inputs (maps, delivery locations, defects) effectively, work in a multi-objective setting and exploit semantic as well as structural properties of places.3. Build LLMs and Foundational models that are specialized for Geospatial domain to perform multitasking (address parsing, validation, normalization, completion, etc.). We also use in-context and retrieval augmented learning to utilize real-world contextual information to ground the model predictions.Key job responsibilities
- Master's degree
- 8+ years of building machine learning models or developing algorithms for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- PhD
- Exposure to research and publications
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