Applied Scientist II, AFT AI, Amazon AFT AI

Are you excited about developing agentic AI, LLM and computer vision models that revolutionize Amazon's Fulfillment network? Are you looking for opportunities to apply state-of-the-art AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network.

Key job responsibilities
In this role, you will build agentic AI solutions and multi-modal deep learning models that understand how products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like understanding warehouse operations systems, or visual defect detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will work with a diverse set of very large multi-modal real-world datasets, including imagery, natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions.

A day in the life
AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary project involving scientists and engineers with deep expertise in developing state-of-the-art AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art agentic AI, deep learning, language understanding and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network.

About the team
Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it.

AFT AI is spread across NA (Bellevue, WA) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location.

Publicly available articles showcasing some of our work:

- Visual Defect Detection: https://www.amazon.science/blog/novel-kaputt-dataset-sets-new-benchmark-for-large-scale-visual-defect-detection
- Eluna: https://www.aboutamazon.com/news/operations/new-robots-amazon-fulfillment-agentic-ai

Basic Qualifications

- PhD, or a Master's degree and experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience in building models for business application
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Strong programming proficiency in Python with production-quality code standards; deep technical expertise with PyTorch and proficiency with the modern ML stack (Pandas, NumPy, scikit-learn, Hugging Face Transformers)
- Demonstrated ability to design and execute end-to-end ML projects from research through production deployment, with experience in model monitoring and iterative improvement
- Strong expertise in modern deep learning architectures including transformers and diffusion models, with hands-on experience in training optimization techniques (distributed training, mixed precision, gradient accumulation) and model compression methods (quantization, pruning, distillation)
- Experience fine-tuning large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen)
- Proven experience developing agentic AI systems using state-of-the-art frameworks (LangChain, Strands, etc.) with ability to design tool-augmented reasoning systems, RAG systems, and advanced prompt engineering techniques (chain-of-thought, few-shot)
- Strong knowledge and hands-on experience across multiple ML domains including computer vision (object detection, segmentation, classification), natural language processing (text generation, information extraction), and multimodal learning
- Understanding of ML systems design including model serving infrastructure, A/B testing frameworks, and MLOps best practices

Preferred Qualifications

- Experience in professional software development
- Experience with explainable machine learning and artificial intelligence methodologies and tools
- Experience working with large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen) in production settings
- Experience collaborating on cross-functional ML initiatives with demonstrated impact on product metrics
- Multiple publications in top-tier venues, including co-authored papers or contributions to ML research communities
- Experience with generative AI techniques including diffusion models for image/video synthesis, autoregressive models for multimodal generation, and controllable generation systems
- Experience with specialized ML domains such as few-shot learning, meta-learning, or domain adaptation; ability to build models that handle distribution shifts or long-tail scenarios

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