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Amazon Robotics GO-AI Operations is seeking an Associate II, ML Data Operations to perform data annotation tasks across text, image, and video data to support ML model training and validation. This role emphasizes accuracy, attention to detail, and the ability to work with diverse data types in a non-technical capacity.
The position is offered on a contractual basis with potential for full-time transition based on performance and business needs, located in Hyderabad, India.
Associate II, ML Data Operations, GO-AI Operations Amazon Robotics develops state-of-the art robotics for Amazon’s Fulfillment Centers. Come build the future of human-technology interaction with us. We are combining computer vision, mobile robots, advanced end-of-arm tooling, and a high degree of freedom of movement to solve real-world problems on a huge scale. Our team collaborates with a varietyof customers across Amazon worldwide to conceive, develop, prototype, and deploy a wide range of robotic systems. Within Amazon Robotics, Global Operations - Artificial Intelligence (GO-AI) enables Amazon to accelerate and scale our machine learning transformation via human-in-the-loop support. As an Associate II, ML Data Operations, you will be a vital Human-in-the-Loop expert in a non-technical role, responsible for executing data annotation tasks across highly diverse portfolios. Your work directly supports the training and validation of machine learning models for Amazon Robotics and Fulfillment Technologies, ensuring the quality and integrity of data which is critical for frontier AI improvements. Your work will involve analyzing processes such as packaging innovation, object manipulation, inventory storage, and sortation automation within fulfillment environments. This role requires strong attention to detail, the ability to make sound judgments using provided resources, and contributes to maintaining fulfillment center quality metrics through precise text/video/image annotation. This role is offered on a contractual basis, with the opportunity to transition into a full-time position based on performance and business requirements.
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