* Research delivery - Working alone and with others, inventing and evaluating new models and algorithms on offline datasets or in the lab. * Research vision - Technical leadership, guiding where the team should focus its efforts. As a Principal Research Scientist, you will drive the research focus, coach other scientists and engineers, perform your own studies, devise new algorithms and deploy them to production. Key job responsibilities The work spans from basic research, such as architecture design and cross-domain training, to experimenting on prototypes in the lab, to running wide-scale A/B tests on robots in our facilities. It requires expertise in both robotics, computer vision and how to train large-scale vision-language foundation models (VLMs) or large language models, using data from many different tasks and scenes. It includes using machine learning to control perception, robot grasping and placement of objects, decision-making and several kinds of defect detection. This includes building multi-viewpoint models and building computer vision systems able to reason about sequences of images or video taken of a scene. The person will work on Foundation Models for computer vision for item understanding and robotic manipulation. This role will work on Scene Perception for Robotic Manipulation and Inventory Quality. We are looking for scientists, engineers and program managers for a variety of roles. We demonstrate its efficacy by evaluating on the problem of click fraud detection on ads to obtain a 9% relative improvement on robot detection metrics over a supervised learning baseline and 4% over a contrastive learning experiment.Īre you excited about developing generative AI and foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real 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 that end, we propose a self supervised pre-training strategy that utilizes Manifold Mixup to produce data augmentations for tabular data and perform reconstruction on these augmentations using noise contrastive estimation and mean absolute error losses, both of which are particularly suitable for large scale tabular data. Tabular data can consist of various types of data with high cardinality and range of feature values especially in a large scale real world setting. But unlike these, designing self supervised learning tasks for tabular data is inherently challenging. Self supervised learning has recently been very effective for pre-training representations in domains such as vision, natural language processing, etc. In this paper, we tackle the problem of self supervised pre-training of deep neural networks for large scale tabular data in online advertising.
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