ucd-gunrock-team-650px.jpg
Location: Davis, CA, USA
Faculty advisor: Zhou Yu

Gunrock (2018)

Named for our university's mascot, Gunrock, our team is a group of students who all share a passion for improving everyday human experiences through artificial intelligence.

Our team consists of 14 graduate and undergraduate computer science and electrical and computer engineering students with diverse, international perspectives. Our fearless leader is Zhou Yu, an assistant professor of computer science who was recently recognized in Forbes' 2018 30 Under 30 in Science list for her research in developing algorithms that enable software to adapt to users. Using our combined knowledge and expertise in developing large-scale distributed computing platforms, sub-systems, machine learning and application software, our team can't wait to use Amazon's platform and user pool to tackle the real-world needs of the general public.

Chun-Yen C. - Team leader

Chun-Yen was a senior software engineer with 5 years hands-on experience specializing on developing a large-scale distributed platform and scalable machine learning systems, in telecommunication company HTC. He received his master's degree in Communication Engineering from National Taiwan University in 2012.

Chun-Yen is currently a first-year master student and a Graduate Student Researcher in computer science department at the University of California, Davis. His main focus is to build a data management framework for the general usage of visualization systems and architect a robust framework for the chatbot system.

Ashwin B.

I am a Masters in Computer Science student studying at the University of California, Davis. I am a passionate programmer having a strong Data Structures and Algorithms knowledge base. In this information age, my research interest lies in Data Science/ Data Analytics. Previously, I have worked with Dell-EMC where I applied the concept of Software Defined Networking to WAN to implement an SD-WAN solution which reduced the network reconfiguration speed by 60%. My hobbies include but are not limited to sketching, writing, reading, hiking, adventure sports and exploring the unknown. I love trying out new things and having new experiences.

Austin C.

I earned my Neuroscience B.S. at University of California, Los Angeles, with a focus on psychology and cognitive science. During my undergraduate, I started learning programming and mobile app development and switched my pursuit to computer science. I have also taken coursework in machine learning and neural networks on top of my major. I am pursuing my masters in computer science at UC Davis focusing on NLP, HCI and dialogue systems and researching under Prof. Zhou Yu. My current project is a dialogue-based movie recommendation system that generates recommendation using matrix factorization and collaborative filtering.

Weiming W.

N/A

Dian Y.

I am a first year PhD student working with Prof. Kenji Sagae on dialogue systems and machine translation at University of California, Davis. We are currently working on dialogue state tracking and parsing. Besides NLP, I am also interested in computer vision. Before this, I earned a B.S. in Computer Science and a B.S. in Finance at New York University. I was advised by Prof. Keith Ross working on reinforcement learning with a focus on natural language processing, as well as researching on computer networking.

Giritheja S.

I am a first year graduate student majoring in Computer Science. I graduated from the National Institute of Technology, Karnataka, India in 2017 with a major in Electrical and Electronics engineering. I have previously worked as a Summer Intern in the Cloud team of Fidelity Investments. I contribute to Open Source organizations involving Software Development. My recent course project involved exploring Deep Learning techniques to recover variable names from minified javascript files, I was intrigued by applications of Deep Learning and AI. I look forward to exploring it.

Kevin J.

I earned my Computer Science B.S and Computer Engineering B.S at the University of California, Santa Cruz. At the University of California, Davis I am currently working with Professor Yu Zhou for a Ph.D. in NLP and dialogue systems. My work in dialogue systems has led me to create a movie recommendation dialogue bot using collaborative filtering and matrix factorization.

Mingyang Z.

Previously, I worked with Professor Jason Corso on video activity segmentation and video classification research problem where I have experience of using sparse coding, CNN and RNN. I also did a humor classification project with Professor Rada Mihalcea to classify whether an image can pair with a humorous punchline to make good memes. Currently, I am working with Professor Yu Zhou at UC Davis for Ph.D, where I worked on the research problem of multimodality machine translation research. I implemented a sequence to sequence model and a visual semantic meaning embedding algorithm as the starting baseline model for this research.

Shreenath I.

I'm a first year Master's student at UC Davis with my areas of research being Software Engineering, Distributed Operating Systems and Machine Learning. I received my Bachelor's in Computer Science in 2015 from the University of Pune and I've worked with Fidelity National Information Services for two years as a Product Development Engineer. I have primarily worked on Python, Django, and Buildbots in a Continuous Integration environment to facilitate the build and release process. I have worked with recommendation systems and language processing before and I look forward to using my experience and building on it through this project.

Yi Mang (Terry) Y.

I am an undergraduate Computer Science major interested in artificial intelligence. Conversational artificial intelligence is enabling a natural and engaging way for people to interact with machines. It is an exciting time but creating a smart socialbot presents many challenges. For our team, I bring my experience in building full-stack software systems that integrate machine learning models. I also have research experience in applying deep learning to computer vision problems.

Antara B.

I'm a first year Master's student at UC Davis in computer science and my research interests lie in machine learning and natural language processing. I completed my undergraduate degree in computer science in 2017 from SRM University and I've worked on computer vision and NLP problems as part of my internship at Medyug Technologies.

Zhou Yu - Faculty advisor

Education: Ph.D in Language Technology Institute, School of Computer Science, Carnegie Mellon University, 2017

B.S. in Computer Science Department & B.A. in the Foreign Language Department with a linguistics focus in Zhejiang University, 2011

Professional Experience: Assistant Professor, University of California, Davis, 2017-present

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CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN
US, WA, Seattle
The Automated Reasoning Group in AWS Platform is looking for an Applied Scientist with experience in building scalable solver solutions that delight customers. You will be part of a world-class team building the next generation of automated reasoning tools and services. AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. You will apply your knowledge to propose solutions, create software prototypes, and move prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You will use your strong verbal and written communication skills, are self-driven and own the delivery of high quality results in a fast-paced environment. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. See https://aws.amazon.com/security/provable-security/ As an Applied Scientist in AWS Platform, you will play a pivotal role in shaping the definition, vision, design, roadmap and development of product features from beginning to end. You will: - Define and implement new solver applications that are scalable and efficient approaches to difficult problems - Apply software engineering best practices to ensure a high standard of quality for all team deliverables - Work in an agile, startup-like development environment, where you are always working on the most important stuff - Deliver high-quality scientific artifacts - Work with the team to define new interfaces that lower the barrier of adoption for automated reasoning solvers - Work with the team to help drive business decisions The AWS Platform is the glue that holds the AWS ecosystem together. From identity features such as access management and sign on, cryptography, console, builder & developer tools, to projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second. Learn and Be Curious. We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members. Inclusion and Diversity. Our team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more. Key job responsibilities Work closely with internal and external users on defining and extending application domains. Tune solver performance for application-specific demands. Identify new opportunities for solver deployment. About the team Solver science is a talented team of scientists from around the world. Expertise areas include solver theory, performance, implementation, and applications. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Portland, OR, USA | Seattle, WA, USA
US, CA, San Diego
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 III 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. We are open to hiring candidates to work out of one of the following locations: San Diego, CA, USA
IN, KA, Bengaluru
How to use the world’s richest collection of e-commerce data to improve payments experience for our customers? Amazon Payments Global Data Science team seeks a Senior Data Scientist for building analytical and scientific solutions that will address increasingly complex business questions in the Gift-Cards space. Amazon.com has a culture of data-driven decision-making and demands intelligence that is timely, accurate, and actionable. This team operates at WW level and provides a fast-paced environment where every day brings new challenges and opportunities. As a Senior Data Scientist in this team, you will be driving the Data Science/ML roadmap for business continuity & growth. You will develop statistical and machine learning models to solve for complex business problems in Gift-Cards space, design and run global experiments, and find new ways to optimize the customer experience. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. You will explore GenAI use-cases within Gift-Cards space and also work on cross-disciplinary efforts with other scientists within Amazon. Key job responsibilities - You should be detail-oriented and must have an aptitude for solving unstructured and ambiguous problems. You should work in a self-directed environment, own tasks and drive them to completion - You should be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions - You should demonstrate thorough technical expertise on feature engineering of massive datasets, exploratory data analysis, and model building using state-of-art ML algorithms - Random Forest, Gradient Boosting, SVM, Neural Nets, DL, Reinforcement Learning etc. You should be aware of automating feedback loops for algorithms in production - You should work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus areas - You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions and build mechanisms that answer those questions We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND
IN, KA, Bangalore
Are you interested in changing the Digital Reading Experience? We are from Kindle Books Team looking for a set of Scientists to take the reading experience in Kindle to next level with a set of innovations! We envision Kindle as the place where readers find the best manifestation of all written content optimized with features that enable them to get the most out of reading, and creators are able to realize their vision to customers quickly and at scale. Every time customers open their content, regardless of surface, they start or restart their reading in a familiar, useful and engaging place. We achieve this by building a strong foundation of core experiences and act as a force multiplier and partner for content creators (directly or indirectly) to easily innovate on top of Kindle's purpose built content experience stack in a simple and extensible way. We will achieve this by providing a best-in-class reading experience, unique content experiences, and remaining agile in meeting the evolving needs and preferences of our users. Our goal is to foster long-lasting reading habits and make us the preferred destination for enriching literary experiences. We are building a In The Book Science team and looking for Scientists, who are passionate about Reading and are willing to take Reading to the next level. Every Book is a complex structure with different entities, layout, format and semantics, with more than 17MM eBooks in our catalog. We are looking for experts in all domains like core NLP, Generative AI, CV and Deep Learning Techniques for unlocking capabilities like analysis, enhancement, curation, moderation, translation, transformation and generation in Books based on Content structure, features, Intent & Synthesis. Scientists will focus on Inside the book content and semantically learn the different entities to enhance the Reading experience overall (Kindle & beyond). They have an opportunity to influence in 2 major phases of life-cycle - Publishing (Creation of Books process) and Reading experience (building engaging features & representation in the book thereby driving reading engagement). Key job responsibilities - 5+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience - Knowledge of programming languages such as C/C++, Python, Java or Perl - Experience programming in Java, C++, Python or related language - You have expertise in one of the applied science disciplines, such as machine learning, natural language processing, computer vision, Deep learning - You are able to use reasonable assumptions, data, and customer requirements to solve problems. - You initiate the design, development, execution, and implementation of smaller components with input and guidance from team members. - You work with SDEs to deliver solutions into production to benefit customers or an area of the business. - You assume responsibility for the code in your components. You write secure, stable, testable, maintainable code with minimal defects. - You understand basic data structures, algorithms, model evaluation techniques, performance, and optimality tradeoffs. - You follow engineering and scientific method best practices. You get your designs, models, and code reviewed. You test your code and models thoroughly - You participate in team design, scoping and prioritization discussions. You are able to map a business goal to a scientific problem and map business metrics to technical metrics. - You invent, refine and develop your solutions to ensure they are meeting customer needs and team goals. You keep current with research trends in your area of expertise and scrutinize your results. - Experience in mentoring junior scientists A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test solutions to improve our experience. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, model development and productionizing the same. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. We are open to hiring candidates to work out of one of the following locations: Banagalore, KA, IND | Bangalore, IND | Bangalore, KA, IND
IN, KA, Bangalore
Are you interested in changing the Digital Reading Experience? We are from Kindle Books Team looking for a set of Scientists to take the reading experience in Kindle to next level with a set of innovations! We envision Kindle as the place where readers find the best manifestation of all written content optimized with features that enable them to get the most out of reading, and creators are able to realize their vision to customers quickly and at scale. Every time customers open their content, regardless of surface, they start or restart their reading in a familiar, useful and engaging place. We achieve this by building a strong foundation of core experiences and act as a force multiplier and partner for content creators (directly or indirectly) to easily innovate on top of Kindle's purpose built content experience stack in a simple and extensible way. We will achieve this by providing a best-in-class reading experience, unique content experiences, and remaining agile in meeting the evolving needs and preferences of our users. Our goal is to foster long-lasting reading habits and make us the preferred destination for enriching literary experiences. We are building a In The Book Science team and looking for Scientists, who are passionate about Reading and are willing to take Reading to the next level. Every Book is a complex structure with different entities, layout, format and semantics, with more than 17MM eBooks in our catalog. We are looking for experts in all domains like core NLP, Generative AI, CV and Deep Learning Techniques for unlocking capabilities like analysis, enhancement, curation, moderation, translation, transformation and generation in Books based on Content structure, features, Intent & Synthesis. Scientists will focus on Inside the book content and semantically learn the different entities to enhance the Reading experience overall (Kindle & beyond). They have an opportunity to influence in 2 major phases of life-cycle - Publishing (Creation of Books process) and Reading experience (building engaging features & representation in the book thereby driving reading engagement). Key job responsibilities - 3+ years of building machine learning models for business application experience - PhD, or Master's degree and 2+ years of applied research experience - Knowledge of programming languages such as C/C++, Python, Java or Perl - Experience programming in Java, C++, Python or related language - You have expertise in one of the applied science disciplines, such as machine learning, natural language processing, computer vision, Deep learning - You are able to use reasonable assumptions, data, and customer requirements to solve problems. - You initiate the design, development, execution, and implementation of smaller components with input and guidance from team members. - You work with SDEs to deliver solutions into production to benefit customers or an area of the business. - You assume responsibility for the code in your components. You write secure, stable, testable, maintainable code with minimal defects. - You understand basic data structures, algorithms, model evaluation techniques, performance, and optimality tradeoffs. - You follow engineering and scientific method best practices. You get your designs, models, and code reviewed. You test your code and models thoroughly - You participate in team design, scoping and prioritization discussions. You are able to map a business goal to a scientific problem and map business metrics to technical metrics. - You invent, refine and develop your solutions to ensure they are meeting customer needs and team goals. You keep current with research trends in your area of expertise and scrutinize your results. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test solutions to improve our experience. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, model development and productionizing the same. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. We are open to hiring candidates to work out of one of the following locations: Bangalore, IND | Bangalore, KA, IND
US, WA, Seattle
Amazon is looking for a strategic, innovative science leader within the Global Talent and Compensation (GTMC) organization to lead an interdisciplinary team charged with developing data-driven solutions to model, automate, and inform high judgement decision making by bringing together science and technology in consumer grade internal talent products. GTMC delivers employee-focused experiences by providing scalable and responsive mechanisms for employees, as well as listening and signaling mechanisms for managers and leaders. They do this through intelligent, flexible, and extensible products and scalable data and science services. They set out to deliver a singular experience supporting multiple employee talent journeys (e.g., onboarding, evaluation, compensation, movement, promotion, exit), to generate and capture signals from product data, surface outliers, increase personalization, and improve the efficacy of “next best action” recommendations, for 1.6 million Amazonians around the world. In this role you will lead multiple research teams across the disciplines of Talent Management, Diversity Equity and Inclusion, and Compensation. You will interface with the most senior leaders at Amazon to develop and deliver on a strategic research roadmap that crosses all lines of Amazon businesses (e.g., Consumer, AWS, Devices, Advertising). This role will then partner with engineering and product management leader to deliver the outcomes of this research in production environments. Successful candidates will have an established background expertise in machine learning with some experience in applying this expertise to the fields of talent management, product management and/or software development. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Are you interested in building, developing, and driving the machine learning technical vision, strategy, and implementation for AWS Hardware? AWS Hardware is hiring a Senior Applied Scientist (AS) to lead the definition and prioritization of our customer focused technologies and services. AWS Hardware is responsible for designing, qualifying, and maintaining server solutions for AWS and its customers as well as developing new cloud focused hardware solutions. You will be a senior technical leader in the existing Data Sciences and Analytics Team, build, and drive the data science and machine learning needed for our product development and operations. As a Senior AS at Amazon, you will provide technical leadership to the teams, organization and products for machine learning. Senior AS’s are specialists with deep expertise in areas such as machine learning, speech recognition, large language models (LLMs), natural language processing, computer vision, and knowledge acquisition, and help drive the ML vision for our products. They are externally aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing the state-of-the-art for improving Amazon’s products and services. The ideal candidate will be an expert in the areas of data science, machine learning, and statistics; specifically in recommendation systems development, classification, and LLMs. You will have hands-on experience leading multiple simultaneous product development and operations initiatives as well as be able to balance technical leadership with strong business judgment to make the right decisions about technology, infrastructure, methodologies, and productionizing models and code. You will strive for simplicity, and demonstrate significant creativity and high judgment backed by statistical proof. Key job responsibilities MS in Data Science, Machine Learning, Statistics, Computer Science, Applied Math or equivalent highly technical field. 10+ years of hands-on experience working in data science and/or machine learning using models and methods such as neural networks, random forests, SVMs or Bayesian classification. 3+ years developing recommendation systems and/or LLMs. 3+ years of experience working in software development, machine learning engineering or ops. Have a history of building highly scalable systems that capture and utilize large data sets in order to quantify your products performance via metrics, monitoring, and alarming. Experience using R, Python, Java, or other equivalent statistics and machine learning tools. Experienced in computer science fundamentals such as object-oriented design, data structures and algorithm design. 3+ years of experience developing in a cloud environment. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Are you a scientist interested in pushing the state of the art in LLMs, ML or Computer Vision forward? Are you interested in working on ground-breaking research projects that will lead to great products and scientific publications? Do you wish you had access to large datasets? Answer yes to any of these questions and you’ll fit right in here at Amazon. We are looking for a hands-on researcher, who wants to derive, implement, and test the next generation of Generative AI algorithms (either LLMs, Diffusion Models, auto-regressors, VAEs, or other generative models). The research we do is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish cutting edge research. In order to achieve our vision, we think big and tackle technology problems that are cutting edge. Where technology does not exist, we will build it. Where it exists we will need to modify it to make it work at Amazon scale. We need members who are passionate and willing to learn. “Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.” Please visit https://www.amazon.science for more information #hltech #hitech Key job responsibilities - Derive novel ML or Computer Vision or LLMs and NLP algorithms - Design and develop scalable ML solutions - Work with very large datasets - Work closely with software engineering teams and Product Managers to deploy your innovations - Publish your work at major conferences/journals. - Mentor team members in the use of your Generative AI and LLMs. About the team We are a tight-knit group that shares our experiences and help each other succeed. We believe in team work. We love hard problems and like to move fast in a growing and changing environment. We use data to guide our decisions and we always push the technology and process boundaries of what is feasible on behalf of our customers. If that sounds like an environment you like, join us. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
GB, Cambridge
The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR