Carnegie Mellon University - Team Tartan.jpg
Location: Pittsburgh, PA, USA
Faculty advisor: Alex Rudnicky

Tartan (2020)

Team Tartan is a mix of software engineers, linguistics researchers, product managers and human-computer interaction designers.

Team Tartan from CMU - Carnegie Mellon's students are participating in the Alexa Prize from 2 different continents and multiple time zones. The pandemic has not been able to stop them from putting their best foot forward. Team Tartan is a mix of software engineers, linguistics researchers, product managers and human-computer interaction designers.

Daksh S. - Team leader

Daksh is a Product Manager, and currently a graduate student at Carnegie Mellon University. He is curious about understanding Human Behavior and Technology's influence on it. In addition to building products that people love, he hosts a podcast, Dollar Gujarati, where he interviews immigrant entrepreneurs and learns about how they built their businesses. In his free time, Daksh loves reading science fiction, fantasy and business books.

Feng-Guang S.

I am currently a Master’s student at Carnegie Mellon University, focusing on Natural Language Processing. I have done a lot of projects related to Machine Learning and Multi-modal. For this project, I will be responsible for the models.

Vaishakh K.

I am an engineer passionate about building reliable and scalable intelligent systems that can solve real problems.

I have 4 years of work experience as a Software Engineer. This has left me enriched with knowledge of engineering solutions and their effective implementation to make them end-user ready, while not compromising on engineering and operational excellence. I also have research experience in the area of Natural Language Processing.

Being a believer in continuous learning, I would like to build on the technical and conceptual skills to build systems that solve everyday problems by easing the interaction between man and machines.

Yue F.

I'm a first-year Ph.D. student in the Department of Computer Science at University College London, affiliated with the Web Intelligence Group of the Centre for Artificial Intelligence. I am grateful to be advised by Prof. Emine Yilmaz. I aim to design systems that robustly and efficiently learn to understand human languages and web data to the end of advancing artificial intelligence web service and web information processing. My current research interests lie in natural language processing, information retrieval, machine learning, and data mining.

Chi C.

I’m a UX and product designer currently finishing a Master of HCI at Carnegie Mellon University, School of Computer Science. My prior experiences include art historian, curatorial researcher, and yoga instructor. I’m always passionate about designing solutions at the intersection of art and technology, and believe any meaningful experience starts from where human connection is built.

Li-Wei C.

I’m Li-Wei, a first year Master of Language Technology studying at Carnegie Mellon University. My research interests are in speech processing and natural language processing, especially applying machine learning as a tool.

Clive G.

A graduate student at Carnegie Mellon University pursuing a Masters in Artificial Intelligence and Innovation. Main areas of interest include NLP and Image Processing.

Sujay K.

I am currently in the MIIS program at Language Technologies Institute in CMU. Before this, I was leading the NLP team at an early stage startup based in Silicon Valley (hypersonix.ai) valued at $200 million. We built a natural language interface over Amazon Redshift for our enterprise clients. Previously, I have worked at Citrix and vernacular.ai (another early stage startup working on multi-lingual speech and NLP for Indian languages). I graduated from PESIT, Bangalore in 2017 with an undergrad degree in Computer Science. As you might have already noticed, I have a penchant for early-stage startups, having been employee no. < 5 at 2 seed-stage startups. I am also an avid biker and trekker. Generally an outdoor person.

Dhruv N.

Dhruv is a Master’s student at the Language Technologies Institute, CMU. He aims to build intelligent multimodal systems that can enhance the human experience. Before coming to CMU, he worked as a Machine Learning Engineer, developing Intent and Entity Recognition systems. In his free time, he enjoys gaming and scuba diving.

Karthik G.

I am currently in the MIIS program at Language Technologies Institute in CMU . Before this I was working as a Machine learning Engineer at Vernacular.ai a series A funded conversational AI startup where I built and deployed multi-lingual SLU systems.My research interests include Spoken language understanding,Reading comprehension based QA systems,Emotion recognition and End-to-End SLU systems.

Jiajun B.

Jiajun is a graduate student at Carnegie Mellon University. His interest lies in multilingual natural language processing. He did his undergraduate at the University of Michigan, majoring in computer science. In his spare time, he likes watching football games.

Alex Rudnicky - Faculty advisor

Alexander is a Professor Emeritus at Carnegie Mellon University, in the Language Technologies Institute of the School of Computer Science. Dr. Rudnicky's research has spanned many aspects of spoken language, including knowledge-based recognition systems, language modeling, spoken language system architectures, multi-modal interaction, analysis of conversational structure, and design principles for speech interfaces. He has been active in research into spoken dialog, and has made contributions to dialog management, language generation, confidence metrics for recognition and understanding and human-robot interaction. Dr. Rudnicky is interested in the induction of concepts and task structure from speech, and proactively acquire of knowledge through dialog.

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US, CA, Santa Clara
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GB, London
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US, WA, Bellevue
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US, WA, Seattle
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US, WA, Bellevue
We are looking for talented Applied Scientists who are adept at a variety of skills, especially with LLMs, use of edge devices, computer vision, or related foundational models that will accelerate our plans to generate high-quality defect detection mechanisms. Our mission is to improve the reliability of equipment (conveyors, motors, belts), and effectively identify from sensors, images, and video specific actions on material handling equipment (MHE) that can prevent unplanned downtime. With millions of products available on Amazon.com comes variation in weight, size, material, and shape. We build products and systems to detect and prevent equipment downtime using a diverse set of classification and anomaly detection algorithms including LLMs. We screen over 150 million events every day, and process this data to create real time alerting systems. We are still day 1 and have an exciting roadmap to build AI predictive maintenance models, deploy scalable causal inference solutions to measure the impact of events, and optimize the reliability of conveyance helping Amazon scale for years to come. As an Applied Scientist II, you will design, develop, and maintain scalable, Artificial Intelligence models with automated training, validation, monitoring and reporting. You will work closely with other scientists and engineers to architect and develop new learning algorithms and prediction techniques. You will collaborate with product managers and engineering teams to design and implement scientific solutions for Amazon problems. Provide technical and scientific guidance to your team members. Contribute to the research community, by working with other scientists across Amazon and publish papers at peer reviewed journals and conferences. Key job responsibilities - Design and implement scalable infrastructure that enables stacked deep learning models to detect a variety of defects in fractions of a second; - Design and implement anomaly detection and large language models to identify defects associated with customer packages; - Experiment and scale models to thousands of sites worldwide; - Collaborate with RME internal and external stakeholders and have a cross-team impact; - Create and share with audiences of varying levels technical papers and presentation. About the team We are a growing team of applied, research, and data scientists working together with an engineering team and product managers to create the next-generation IIoT platform for the Reliability and Maintenance Engineering org.
US, NY, New York
Amazon continues to invest heavily in building our world class advertising business. Our products are strategically important to our Retail and Marketplace businesses, driving long term growth. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and strong bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.The Sponsored Products OSSR team is responsible for all non-search supply and associated experiences. As an Applied Scientist on our team, you will be responsible for defining the science and technical strategy for one of our most impactful strategic initiatives, creating lasting value for Amazon and our advertising customers. Key job responsibilities • Support business, science and engineering strategy and roadmap for Sponsored Products OSSR projects • Drive alignment across organizations for science, engineering and product strategy to achieve business goals • Lead/guide scientists and engineers across teams to develop, test, launch and improve of science models designed to optimize the shopper experience and deliver long term value for Amazon and advertisers • Develop state of the art experimental approaches and ML models. About the team Sponsored Products (SP) is Amazon's largest and fastest growing business. Over the last few years we grown to a multi-billion dollar business. SP ads are shown prominently throughout search and detail pages, allowing shoppers to seamlessly discover products sold on Amazon. Ad experience and market place is one of the highest impact decisions we make. This role has unparalleled opportunity to grow our marketplace and deliver value for advertisers and shoppers.
US, WA, Seattle
Do you want to join an innovative team of scientists who use deep learning, natural language processing, large language models to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support and provide the best customer and seller experience by automatically mitigating risk? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience? Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and simplify processes? If yes, then you may be a great fit to join the Machine Learning Accelerator team in the Amazon Selling Partner Services (SPS) group. Key job responsibilities The scope of an Applied Scientist II in the Selling Partner Services (SPS) Machine Learning Accelerator (MLA) team is to research and prototype Machine Learning applications that solve strategic business problems across SPS domains. Additionally, the scientist collaborates with engineers and business partners to design and implement solutions at scale when they are determined to be of broad benefit to SPS organizations. They develop large-scale solutions for high impact projects, introduce tools and other techniques that can be used to solve problems from various perspectives, and show depth and competence in more than one area. They influence the team’s technical strategy by making insightful contributions to the team’s priorities, approach and planning. They develop and introduce tools and practices that streamline the work of the team, and they mentor junior team members and participate in hiring.
US, WA, Seattle
An information-rich and accurate product catalog is a strategic asset for Amazon. It powers unrivaled product discovery, informs customer buying decisions, offers a large selection, and positions Amazon as the first stop for shopping online. We use data analysis and statistical and machine learning techniques to proactively identify relationships between products within the Amazon product catalog. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality). Amazon’s Item and Relationship Identity Systems group is looking for an innovative and customer-focused applied scientist to help us make the world’s best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will work in a collaborative environment where you can experiment with massive data from the world’s largest product catalog, work on challenging problems, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers. Key job responsibilities * Map business requirements and customer needs to a scientific problem. * Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization. * Research, design and implement scalable machine learning (ML) techniques to solve problems that matter to our customers in an iterative fashion. * Design, experiment and evaluate highly innovative models for predictive, explainable learning * Partner with other scientists to build state-of-the-art ML systems powering Amazon * Work closely with software engineering teams to drive real-time model experiments, implementations and new feature creations * Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities. About the team The IRIS team owns programs and systems to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog. We focus on the following areas: 1) reducing customer perceived duplicates: eliminating all duplicate ASINs that are indistinguishable by customers and identifying broken and missing variations, 2) reducing product detail page inconsistency: preventing inconsistent item identities, and improving the customer experience by automatically detecting and creating factual relationships between ASINs: e.g. variation families, newer versions, 3) reducing selling partner listing friction: reducing GTIN defects in the catalog, and false conflicts in contributions, and 4) improving brand customer experience: providing a strong brand identity to contributions and ASINs, by matching them to Universal Brand Catalog brand entities.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences