Applied Scientist III, AFT AI, Amazon AFT AI
Amazon (AI roles) · Berlin, Berlin, DEU
No salary listed. Estimated from 259 salary-disclosed Research roles on this board: $235K–$325K (interquartile range; estimate, not the employer's figure).
Apply at Amazon (AI roles)Find your warm intro on LinkedInAbout the role
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 - 5+ years of relevant, broad research experience after a PhD degree or equivalent qualification
- Track record of first-author publications at top-tier peer-reviewed conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP) or patents in machine learning domains
- Expert-level programming proficiency in Python with production-quality code standards, plus working knowledge of C++ for performance-critical applications; deep technical expertise with PyTorch and proficiency with the modern ML stack (Pandas, NumPy, scikit-learn, Hugging Face Transformers)
- Proven ability to independently scope, design, and execute end-to-end ML projects from research through production deployment, including ownership of model monitoring, maintenance, and iterative improvement
- Proven expertise in modern deep learning architecture design including transformers, diffusion models, and neural architecture search, with hands-on experience in designing and training self-supervised learning paradigms, training optimization techniques (distributed training across multi-node GPU clusters, mixed precision, gradient accumulation, parallelism strategies using DeepSpeed, FSDP, or Megatron-LM), and model compression methods (quantization, pruning, distillation)
- Proven experience pre-training and fine-tuning large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen)
- Proven experience developing agentic AI systems deployed to production, using state-of-the-art frameworks (LangChain, Strands, etc.) with proven ability to design multi-agent workflows, tool-augmented reasoning systems, RAG systems and advanced prompt engineering techniques (chain-of-thought, few-shot, RLHF, DPO)
- Extensive knowledge and proven production experience across multiple ML domains including computer vision (object detection, segmentation, 3D vision, depth estimation, point cloud processing), natural language processing (text generation, information extraction), and multimodal learning
- Strong understanding of ML systems design including model serving infrastructure, A/B testing frameworks, feature stores, and MLOps best practices, such as annotation pipeline design, active learning pipelines, and AutoML/hyperparameter optimization techniques
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 - 5+ years of relevant, broad research experience after a PhD degree or equivalent qualification
- Track record of first-author publications at top-tier peer-reviewed conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP) or patents in machine learning domains
- Expert-level programming proficiency in Python with production-quality code standards, plus working knowledge of C++ for performance-critical applications; deep technical expertise with PyTorch and proficiency with the modern ML stack (Pandas, NumPy, scikit-learn, Hugging Face Transformers)
- Proven ability to independently scope, design, and execute end-to-end ML projects from research through production deployment, including ownership of model monitoring, maintenance, and iterative improvement
- Proven expertise in modern deep learning architecture design including transformers, diffusion models, and neural architecture search, with hands-on experience in designing and training self-supervised learning paradigms, training optimization techniques (distributed training across multi-node GPU clusters, mixed precision, gradient accumulation, parallelism strategies using DeepSpeed, FSDP, or Megatron-LM), and model compression methods (quantization, pruning, distillation)
- Proven experience pre-training and fine-tuning large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen)
- Proven experience developing agentic AI systems deployed to production, using state-of-the-art frameworks (LangChain, Strands, etc.) with proven ability to design multi-agent workflows, tool-augmented reasoning systems, RAG systems and advanced prompt engineering techniques (chain-of-thought, few-shot, RLHF, DPO)
- Extensive knowledge and proven production experience across multiple ML domains including computer vision (object detection, segmentation, 3D vision, depth estimation, point cloud processing), natural language processing (text generation, information extraction), and multimodal learning
- Strong understanding of ML systems design including model serving infrastructure, A/B testing frameworks, feature stores, and MLOps best practices, such as annotation pipeline design, active learning pipelines, and AutoML/hyperparameter optimization techniques
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