• Experience (non-internship) in professional software development
• Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
• Experience programming with at least one software programming language
• Practical experience with cloud services (e.g. AWS, Microsoft Azure, Google Cloud)
• Excellent knowledge of MLOps, Data Ops and relevant fields

Are you excited about developing state-of-the-art computer vision models that revolutionize Amazon’s Fulfillment network? Are you looking for opportunities to apply 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 a Machine Learning Engineer who will build ML and data infrastructure to support our science team in training and deploying 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

• Responsible for the design, development, and maintenance of scalable ML, data and annotation infrastructure needed for training, evaluating and deploying models for real-world applications. Work closely with Applied Scientists to process massive data, scale machine learning models while optimizing model performance and efficiency.
• Design and implement software solutions balancing conflicting requirements of delivery speed, scalability, flexibility, production readiness.
• Work with other team members (Applied Scientists, System Engineers, SDEs, BIEs, Product and Program Managers) to investigate design approaches, prototype new technologies and evaluate technical feasibility.

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 team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work on critical MLOps infrastructure for model training, model inference and data annotation. You will work with product teams 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 multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location.

Publicly available articles showcasing some of our work:

• Damage Detection: https://www.amazon.science/latest-news/the-surprisingly-subtle-challenge-of-automating-damage-detection
• Product ID: https://www.amazon.science/latest-news/how-amazon-robotics-is-working-on-new-ways-to-eliminate-the-need-for-barcodes
• Master's degree in computer science or equivalent
• Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
• Experience in deploying deep learning models in production
• Experience with AWS (S3, EC2, CDK, Sagemaker etc.)
• Experience with state-of-the-art deep learning and data science frameworks like Py Torch, Tensor Flow, Pandas

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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