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
Basic Qualifications
- 3+ years of building machine learning models or developing algorithms for business application experience
- PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred Qualifications
- Experience with LLMs, VLMs, or foundation models—including fine-tuning, prompt engineering, or retrieval-augmented generation
- Experience or familiarity with the travel and events domain—including corporate travel management, booking systems, supplier negotiations, travel expense and compliance analytics
- Familiarity with model optimization techniques such as distillation, quantization, or efficient inference strategies
- Experience working with large-scale datasets and distributed computing frameworks (Spark, Ray, or equivalent)
- Exposure to multimodal learning (text + image) or computer vision techniques
- Experience with explainable AI, model interpretability, or uncertainty quantification
- Publications in ML/AI conferences or journals (NeurIPS, ICML, ICLR, ACL, CVPR, etc.) are a plus
- Strong experimental design skills and statistical analysis expertise
- Excellent written and verbal communication skills
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