Amazon

Applied Scientist II, Last Mile Address Intelligence (LMAI)

Posted On

March 19, 2025

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Machine Learning Science

Apply By

July 1, 2025

Job Description

Customer addresses, Geospatial 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.

The Role: As an Applied Scientist II, you will tackle complex challenges in address normalization, geospatial analysis, and predictive modeling for delivery operations. You will work on enterprise-scale machine learning solutions, developing and implementing sophisticated algorithms that directly impact business outcomes. This role offers the opportunity to innovate with state-of-the-art technologies, including large language models and advanced AI applications.

Technical Scope: Your work will encompass address intelligence, involving complex data processing across multiple languages and regions. You'll develop machine learning frameworks that handle geographical relationships, location attributes, and routing optimizations. The role includes working with diverse data sources, including mapping information, geographical datasets, and various location identifiers.

The ideal candidate will possess deep knowledge of machine learning methods for large-scale predictive modeling, natural language processing, and graph-based learning. Experience in productionizing ML models and good communication skills are essential. You should be comfortable explaining complex technical concepts to diverse stakeholders and working collaboratively in a fast-paced environment. This position offers significant opportunities for professional development, including working on multi-year complex problems, publishing research in prestigious ML conferences, and developing innovative solutions using technologies. You'll have end-to-end ownership of projects from design through implementation, with the chance to make meaningful contributions to large-scale logistics operations.

Key job responsibilities
As an Applied Scientist II, 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.

About the team
Last Mile Address Intelligence (LMAI) team owns WW charter for address and location learning solutions which are crucial for efficient Last Mile delivery planning. The team works out of Hyderabad and Bangalore offices in India. LMAI is a part of Geospatial science team, which also owns problems in the space of maps learning and travel time estimations. The rest of the Geospatial science team and senior leadership of Last Mile org works out of Bellevue office.- 3+ years of building models for business application experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

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