Are you a MS or PhD student interested in a 2022 Applied Science Internship in the field of Computer Vision, or Machine Learning/Deep Learning?
Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way?
If this describes you, come join our research teams at Amazon.
As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyse and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.
We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelise, and implement state-of-the-art solutions for never-before-solved problems.
Team Name: Alexa Knowledge Spoken Language Understanding
Team Description: Alexa AI's North Star is to advance general AI, and in Alexa AI Knowledge we strive to be the world's most knowledgeable and trusted assistant. Our customers expect Alexa to recognise their speech despite accent or sound conditions, and to answer any factual question about the world around them. We ensure that Alexa's speech-to-text for factual questions is accurate, so that they can get the answers to their questions when they need them.
Team Name: yhat (EU AS APP)
Domain/Research Focus: Time series forecasting, predictive models, decision making
Team description: We leverage Machine Learning to support the Transportation services. Our problems include forecasting the flows of packages in Amazon’s global network, detecting anomalies in time series data, reinforcement learning to make real time decisions and natural language processing to streamline access and exploration of business metrics.
Team Name: Prime Video Automated Reasoning
Domain/Research Focus: Automated Reasoning, Static Analysis, Formal Verification
Team Description: Our team applies deep and cutting-edge automated reasoning techniques (such as static analysis, formal verification) to detect defects automatically in Prime Video’s core systems, and enable our customers (engineers across Prime Video) to assess the quality and correctness of their software at different stages of the code authorship workflow, such as code review and local development environments. The tools we build are essential to the software development and release cycle of many Prime Video engineering organisations, and will represent a huge step forward in the sophistication of our approach to automated quality assurance.
Team Name: Prime Video Anomaly Detection & Insights
Domain/Research Focus: Deep Learning, Time Series Forecasting, Predictive Modelling, Causation
Team Description: We obsess over all aspects of the Prime Video customer experience to ensure teams have real time awareness of unexplained consumer behaviour across the thousands of combinatory features, capabilities, devices or regions that we provide our content. We leverage a range of techniques such as deep learning to develop ML models that enable us reliably escalate and inform our internal teams or external partners. Our current challenges include modelling of large scale sporting events, developing causation engines and leveraging disperse data sources to continually improve our forecasting and detection capabilities.
Team Name: Video Quality Analysis (VQA)
Domain/Research Focus: Computer Vision/Machine Learning
Team Description: We take on research to detect quality issues that customers can see or hear in audio video. We use machine learning algorithms, specifically deep-learning combined with classification, signal processing and computer vision techniques to build solutions at scale. Our research covers industry unsolved problems such as detecting AV synchronisation, so we can automate monitoring thousands of linear channels and tens of thousands of live events annually.
Team Name: Alexa Shopping Team
Domain/Research Focus: Information Retrieval, Natural Language Processing
Team Description: As an Applied Scientist in the Alexa Shopping team, you will be investigating and developing information retrieval and natural language processing technologies to help Alexa turn into the best and most trustworthy personal assistant.
The team you will specifically be working with has expertise in information retrieval, natural language processing, crowdsourcing, deep learning, user modelling, probabilistic modelling and other related fields. We employ all these technologies to continually improve the experience of Alexa customers, focusing on the shopping domain. Your work within this team will combine research on information retrieval and natural language processing, exploration of new technologies, systems and software development, as well as publications and presentations at top scientific conferences.
Team Name: The Automated Reasoning Group
Domain/Research Focus: Automated Reasoning
Team Description: The Automated Reasoning Group (ARG) applies mathematical logic to answer questions about the future behaviour of computer systems. ARG’s Redrock Team develops techniques and environments to facilitate the design and implementation of correct distributed services. Our work is concerned with all aspects of distributed systems: modeling and implementation languages, architecture, simulation, testing, verification, and debugging.
Amazon internships are full-time (38 hours/week) and normally run for 8-12 consecutive weeks with timelines dependent on country locations.
This is not a remote internship opportunity.
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