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Applied Scientist Ii, Last Mile Geospatial Science
3 weeks ago
Master's degree
- 4+ years of practical machine learning experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
Customer addresses, Gespatial information and Road-network play a crucial role in Amazon Logistics' Delivery Planning systems. We own exciting science problems in the areas of Address Normalization, Geocode learning, Maps learning, Time estimations including route-time, delivery-time, transit-time predictions which are key inputs in delivery planning. As part of the Geospatial science team within Last Mile, you will partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact. The setting also gives you an opportunity to think about a complex large-scale problem for multiple years and building increasingly sophisticated solutions year over year. In the process there will be opportunity to innovate, explore SOTA and publish the research in internal and external ML conferences.
Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis:
1. De-duping and organizing addresses into hierarchy while handling noisy, inconsistent, localized and multi-lingual user inputs. We do this at the scale of millions of customers for established (US, EU) as well as emerging geographies (IN, MX). We make use of technologies like LLMs, Weak supervision, Graph-based clustering & Entity matching. We also use additional modalities such as building outlines in maps, street view images and 3P datasets, gazetteers.
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.
4. (Work done in sister teams) Developing systems to consume inputs from areal imagery and optimize our maps to enable efficient delivery planning. Also building models to estimate travel time, turn costs, optimal route and defect propensities. For these problems, we make use of multiple CV, Optimization (TSP), Counterfactual analysis and other supervised learning techniques that can operate at scale.
Hyderabad, TS, IND
- PhD
- Exposure to research and publication in top-tier conferences
