Applied Scientist II, Amazon Travel & Events

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Applied Scientist II, Amazon Travel & Events Bengaluru, Karnataka, IND We are looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build intelligent, AI-driven solutions that transform how Amazon manages travel and events at scale. As part of the Amazon Travel & Events (AT&E) Program Technology Solutions team, our mission is to provide a seamless and delightful experience for Amazon's business travellers and events programs by raising the bar in Generative AI with Large Language Models (LLMs), Natural Language Understanding (NLU), conversational AI, and Applied Machine Learning (ML). You will work alongside experienced engineers to develop and apply algorithms and modelling techniques that advance the state-of-the-art in conversational AI, intelligent automation, and data-driven decision making. You will gain hands-on experience with Amazon's heterogeneous travel data sources, including contracts, booking systems, supplier data, and event logistics-and large-scale computing resources to accelerate advances in travel and events intelligence at scale. You will also help make it easier for internal customers to use analytics to monitor and model program performance improvements. Key job responsibilities • Design, develop, and evaluate ML models leveraging GenAI, multimodal reasoning, and large-scale information retrieval to solve well-defined catalog understanding challenges such as product identity and relationship inference • Apply and adapt VLMs, foundation models, and LLM-based approaches to address product catalog problems-experimenting with fine-tuning, prompt engineering, and retrieval-augmented generation techniques • Implement model optimization techniques-including distillation, quantization, and serving optimizations-to improve latency, cost, and efficiency of deployed models under guidance from

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