Posted 16 May, 2026
Applied Scientist II, Amazon Travel & Events
Amazon
IN, KA, Bengaluru
Full Time
Reference: 71_457715_bbb51a35-288e-498e-b0d4-325db7f16bf5
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 senior scientists
Drive the design and execution of rigorous experiments and ablation studies on large-scale datasets, delivering results with statistical rigor and clear recommendations to the team
Build and iterate on ML pipelines from prototyping through production deployment, writing clean, well-tested, production-quality code
Contribute to improving model reliability by applying uncertainty calibration, confidence estimation, and interpretability techniques to support trustworthy catalog decisions
Collaborate closely with senior scientists, engineers, and product teams to translate business requirements into well-scoped ML solutions
Stay current with the latest research in GenAI, VLMs, and multimodal AI, and identify opportunities to apply new techniques to team problems
Co-author research publications and contribute to internal tech talks and knowledge-sharing initiatives
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 senior scientists
Drive the design and execution of rigorous experiments and ablation studies on large-scale datasets, delivering results with statistical rigor and clear recommendations to the team
Build and iterate on ML pipelines from prototyping through production deployment, writing clean, well-tested, production-quality code
Contribute to improving model reliability by applying uncertainty calibration, confidence estimation, and interpretability techniques to support trustworthy catalog decisions
Collaborate closely with senior scientists, engineers, and product teams to translate business requirements into well-scoped ML solutions
Stay current with the latest research in GenAI, VLMs, and multimodal AI, and identify opportunities to apply new techniques to team problems
Co-author research publications and contribute to internal tech talks and knowledge-sharing initiatives
