Applied Scientist, Last Mile Geospatial Science

Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling, natural language processing, semi-supervised & graph based learning. We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise.

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.

3. (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.

Key job responsibilities

As an Applied Scientist I, your responsibility will be to deliver on a well defined but complex business problem, explore SOTA technologies including GenAI and customize the large models as suitable for the application. Your job will be to work on a end-to-end business problem from design to experimentation and implementation. There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences.