ADCI
Applied Science, finance
AppliedScientistII
Neural analysis suggests this role is
optimal for Mid candidates.
“Applied Scientist II at ADCI. Skills: Machine Learning, GenAI, LLMs, Deep learning. Design ML models. Develop ML models”
What You'll Achieve.
Transform how Amazon manages travel; Transform how Amazon manages events; Provide seamless experience; Provide delightful experience; Raise the bar in Generative AI; Advance state-of-the-art; Accelerate advances in intelligence
Industry & Context.
Data-driven decision making; Problem solving; Statistical algorithms; ML solutions development
What They're Looking For.
Must Have
3+ years of building machine learning models, Master's degree and 3+ years of CS, CE, ML or related field experience, Experience developing and implementing deep learning algorithms, Experience in solving business problems through machine learning, Experience in algorithms and data structures, Experience in parsing, Experience in numerical optimization, Experience in data mining, Experience in parallel and distributed computing, Experience in high-performance computing
Nice to Have
Experience with LLMs, VLMs, or foundation models, Experience or familiarity with the travel and events domain, Familiarity with model optimization techniques, Experience working with large-scale datasets, Exposure to multimodal learning, Experience with explainable AI, Publications in ML/AI conferences or journals, Experimental design skills, Statistical analysis expertise
What You'll Do.
Adapt foundation models
Apply LLM-based approaches
Experiment with fine-tuning
Experiment with prompt engineering
Experiment with retrieval-augmented generation
Implement model optimization techniques
Drive design of experiments
Drive execution of experiments
Deliver results with statistical rigor
Provide clear recommendations
Iterate on ML pipelines
Write production-quality code
Contribute to improving model reliability
Apply uncertainty calibration
Apply confidence estimation
Apply interpretability techniques
Support trustworthy catalog decisions
Collaborate with senior scientists
Collaborate with engineers
Collaborate with product teams
Translate business requirements
Stay current with research
Identify opportunities to apply new techniques
Co-author research publications
Contribute to internal tech talks
Contribute to knowledge-sharing initiatives
How You'll Work.
Team & Collaboration
Senior scientists; Engineers; Product teams
Communication Scope
Written communication; Verbal communication
Full Job Description
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
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