International Women's Day 2020.png
Credit: Glynis Condon

Seven Amazon scientists shaping the future of AI

To commemorate International Women’s Day, we spoke to women scientists across a variety of research areas at Amazon.

To commemorate International Women’s Day (IWD), during Women's History Month, we asked scientists across a variety of Amazon research areas about their backgrounds, and the most exciting innovations in their fields. Here’s what they had to say.

Xin Luna Dong, principal scientist

Xin Luna Dong
Xin Luna Dong, principal scientist

Dong is a principal scientist, leading the efforts to develop the Amazon Product Knowledge Graph. Dong received her PhD in computer science, with a focus on data integration, from the University of Washington. The personal information management system in Dong’s dissertation (which won the Best Demo award in Sigmod’ 2005), is a personal knowledge graph developed at least five years before the phrase “knowledge graph” was coined. After graduation, Dong led the development of the Knowledge-based Trust project at Google. Dong has co-authored the book Big Data Integration. She is an ACM Distinguished Member, and has received the VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion”. Dong was program committee co-chair for Sigmoid 2018, and is program committee co-chair for VLDB 2020. She also serves on the VLDB endowment and PVLDB advisory committees.

Innovations I find exciting

A recent innovation that I’m most excited about is graph neural network (GNN). Unlike recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which focus much more on regular data such as word sequences, 2-D images, and 3-D videos, GNNs allow us to leverage graphs to capture much more complex relationships. These include elements in the graph, represented by nodes in the graph, and their relationships, represented by edges between the nodes. Examples of graphs influenced by GNNs include social networks, world wide web (WWW) topology, knowledge graphs, and molecular graphs. As we build knowledge graphs for products, it is amazing how many different ways we can benefit from GNNs.

Naturally, we can apply GNNs on the knowledge graphs we have built, to discern interesting patterns to find popular artists in the music domain. We also model webpage layouts as graphs, and model customer behaviors as graphs, so GNNs help us extract relevant knowledge and enrich our knowledge graphs. This new technique enables us to be so much more creative in the practice of constructing knowledge graphs, and applying the findings to real-world applications.

Claire Law, senior technical program manager

ClaireLaw.jfif
Claire Law, senior technical program manager

Law is senior technical program manager on Amazon’s physical retail team, the team behind the Just Walk Out technology used in Amazon Go and Amazon Go Grocery stores. She studied nanotechnology engineering at the University of Waterloo. Early on, she realized that she didn’t enjoy the type of lab work expected from a researcher in material science. She leveraged a university program, and interned as a software developer, marketer, hardware test engineer, project control officer in consulting, and software program manager. These experiences, coupled with work experiences at Microsoft and Research in Motion, led Law to pursue a career in software.

After a stint in Amazon’s international organization, Law joined the physical retail team to work on machine vision initiatives. On this team, Law is able to leverage her experience in cloud computing and knowledge of optics and photography to build new experiences for physical retail.

Innovations I find exciting

We are only now reaching a level where computer vision can solve real-world problems in a meaningful way. While we still need to be creative in where we look for simplifiers, algorithms are able to solve more and more problems every day. Challenges that looked insurmountable just a couple years ago are now part of production systems across the industry. Checkout-free stores seemed like science fiction before Amazon Go was launched, and now customers are loving this effortless shopping experience in the 25 Amazon Go stores, and the new Amazon Go Grocery store we have open today.

Yoelle Maarek, vice president

Yoelle Maarek, vice president of research and science for Alexa Shopping
Yoelle Maarek, vice president of research and science for Alexa Shopping

Maarek is vice president of research and science, Alexa Shopping. Prior to Amazon, Maarek served in engineering and research leadership roles at Yahoo, Google and IBM. Maarek has been regularly serving as program committee (PC) chair and senior PC committee member at leading academic research conferences related to Web search and data mining, such as SIGIR, The Web Conference, and Web Search and Data Mining (WSDM). She is currently serving on the steering committees of WSDM and the Web Conference series.

She is a member of the Technion Board of Governors and was inducted as an ACM Fellow in 2013. Maarek obtained a PhD in computer science from the Technion, Israel in 1989. She holds an engineering degree from the Ecole des Ponts et Chaussées, and a DEA (graduate degree) in computer science from Paris VI university. Maarek completed her PhD at the Technion in Israel and was a visiting student at Columbia University. She played a pioneering role within industry in researching the field of information retrieval, the computer science discipline behind search, in the pre-Web era, and led the launch of Google Suggest, the query auto-completion capability. As such, she jokingly refers to herself as a “search dinosaur”.

Innovations I find exciting

We are on the verge of making ambient computing happen, and Alexa is pioneering this long-awaited revolution. It forces us to revisit all our assumptions across multiple domains. I see this prevalent especially in search and question answering. These are topics close to my heart. I have been following progress in these areas since I got my PhD thirty years ago. The focus on ambient computing is also a unique opportunity for us at Amazon to demonstrate what we mean by customer-obsessed science. As humans are learning to interact with machines, their behavior is evolving and we need to follow suit. It not only challenges scientists to keep inventing on behalf of customers but also forces all of us to remain humble. We are not here to teach customers how to speak to a machine, but rather to do everything in our power to understand, satisfy and predict their needs so as to constantly wow and delight them.

Angeliki Metallinou, applied science manager

angeliki.jpeg
Angeliki Metallinou, applied science manager

Metallinou is an applied science manager within the Amazon Alexa AI Natural Understanding group. She received both her PhD, and master’s degree in electrical engineering from the University of Southern California. Her interests and experience lie in the areas of spoken and natural language understanding, dialogue systems, machine learning, deep learning, affective computing and applications for education and healthcare.

She has published papers in the areas of speech, language, dialogue, artificial intelligence and multimodal human computer interaction at leading science conferences such as Interspeech, the AAAI Conference on Artificial Intelligence, and the Association for Computational Linguistics (ACL), has served as an area chair for Interspeech 2016, and as a reviewer of papers for several science conferences.

Innovations I find exciting

It is exciting to see how new techniques in deep learning continuously push the boundaries of the state of the art in the fields of dialogue and spoken language processing. I’m very interested in advances around unsupervised, semi-supervised and transfer learning, which allow deep learning models to leverage the power of large corpora without relying on costly and time-consuming manual annotations. Pre-trained language models like BERT and GPT-2 and their use in downstream applications are just a few examples. These innovations are particularly relevant for industry applications where scalability is key.

I am also excited about recent literature in deep learning that is allowing us to develop models to perform complex tasks like higher-level reasoning, for example, over the contents of a document or an image or both, as opposed to simpler classification tasks. I’m also excited to see how these methods can have a positive impact on people through their deployment in products, especially in applications of healthcare, accessibility and education.

Priya Ponnapalli, principal deep learning scientist

Priya Ponnapalli
Priya Ponnapalli, principal deep learning scientist

Ponnapalli is a senior manager and principal deep learning scientist within the Amazon ML Solutions Lab, where she leads a global team of data scientists that help AWS customers accelerate their adoption of ML and cloud technologies across industries, from healthcare and finance to sports. As the leader of Amazon ML Solutions Lab’s sports business, Ponnapalli works with customers including National Football League (NFL), Six Nations Rugby, and Formula 1 (F1), just to name a few, to enhance the fan experience and transform sports using ML.

Ponnapalli is also a senior research affiliate at the Genomic Signal Processing Lab at the University of Utah, and a faculty member at Rutgers Business School, where she teaches ML to business leaders, and works to inspire the next generation of leaders. Prior to joining AWS, she co-founded Eigengene, a data-driven personalized medicine startup and has helped companies like Genentech and Roche establish and build data science teams. For her PhD in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.

Innovations I find exciting

As an Amazon ML Solutions Lab scientist, I’m most excited about real-world applications of ML across industries. I’m interested in innovations to overcome challenges with small, limited datasets that companies often have to contend with. I’m also intrigued by model interpretability and explainability which are key to earning trust and spurring broad adoption. I’m passionate about making ML accessible to all, so it can be used to solve some of the most important problems we are facing, from fighting climate change to treating cancer.

Ana Pinheiro Privette, senior program manager

ana.jpg
Ana Pinheiro Privette, senior program manager

Ana Pinheiro Privette is a senior program manager with Amazon's Sustainability group. She joined the Sustainability Science and Innovation team in September 2017 as the program lead for the Amazon Sustainability Data Initiative (ASDI), a program that seeks to leverage Amazon’s scale, technology, and infrastructure to help create more global innovation for sustainability. ASDI is a Tech-for-Good project and is a joint effort between Amazon Sustainability and the AWS Open Data team focusing on democratizing access to key data and analytical capabilities to anyone working in the sustainability space.

Privette was trained as an environmental engineer and as an earth sciences researcher at the New University of Lisbon (Portugal) and at MIT. She did her doctoral research work at NASA in the Washington D.C. area and as part of her project, she spent a couple of years running scientific field work sites in Africa to support a NASA international field campaign. After spending most of her career at NASA and NOAA as a scientist, Privette led projects for the White House climate portfolio, including the Obama Climate Data Initiative and the Partnership for Resilience and Preparedness (PREP), both focused on delivering better access and use of US Federal climate data to support decision makers.

Innovations I find exciting

As part of ASDI, I work very closely with AWS customers developing applications in the space of sustainability to understand what challenges they may be experiencing and how we may accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. The ASDI currently works with scientific organizations like NOAA, NASA, the UK Met Office and Government of Queensland to identify, host, and deploy key datasets on the AWS Cloud, including weather observations, weather forecasts, climate projection data, satellite imagery, hydrological data, air quality data, and ocean forecast data. These datasets are publicly available to anyone.

In addition, ASDI provides cloud grants to those interested in exploring the use of AWS’ technology and scalable infrastructure to solve big, long-term sustainability challenges with this data. The dual-pronged approach allows sustainability researchers to analyze massive amounts of data in mere minutes, regardless of where they are in the world or how much local storage space or computing capacity they can access.

Nashlie Sephus, manager, applied science

sephus.jpg
Nashlie Sephus, applied scientist, Amazon Web Services machine learning team.
Credit: Terrence Wells@PoetWilliamsPhotography

Sephus is an applied scientist on AWS’s artificial intelligence team, focusing on computer vision. In this role, Sephus focuses on the fairness and accuracy of the team’s algorithms. Sephus formerly led the Amazon Visual Search team in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. This technology was a result of former startup Partpic (Atlanta) being acquired by Amazon, for which she was the chief technology officer (CTO). Prior to working at Partpic, she received her PhD in 2014 from the School of Electrical and Computer Engineering at the Georgia Institute of Technology. She received her bachelor’s degree in computer engineering in 2007 from Mississippi State University.

Innovations I find exciting

Since the onset of machine learning and artificial intelligence, neural networks (such as convolutional neural networks (CNNs), and generative adversarial networks (GANs), etc.) and learning algorithms have always excited me. It’s being able to quickly and automatically draw patterns from data, whether it be images, video, or audio at scale, that fascinates me. Since music was my first love (along with karaoke!), music information retrieval has always been a passion of mine. These innovations, when used responsibly and fairly, are able to benefit people in their everyday activities.

Research areas

Related content

US, VA, Herndon
Do you love decomposing problems to develop machine learning (ML) products that impact millions of people around the world? Would you enjoy identifying, defining, and building ML software solutions that revolutionize how businesses operate? The Global Practice Organization in Professional Services at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex ML products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud. Key job responsibilities Our ML Engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You’ll bring a passion for the intersection of software development with generative AI and machine learning. You’ll also: - Solve complex technical problems, often ones not solved before, at every layer of the stack. - Design, implement, test, deploy and maintain innovative ML solutions to transform service performance, durability, cost, and security. - Build high-quality, highly available, always-on products. - Research implementations that deliver the best possible experiences for customers. A day in the life As you design and code solutions to help our team drive efficiencies in ML architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also: - Build high-impact ML solutions to deliver to our large customer base. - Participate in design discussions, code review, and communicate with internal and external stakeholders. - Work cross-functionally to help drive business solutions with your technical input. - Work in a startup-like development environment, where you’re always working on the most important stuff. About the team The Global Practice Organization for Analytics is a team inside the AWS Professional Services Organization. Our mission in the Global Practice Organization is to be at the forefront of defining machine learning domain strategy, and ensuring the scale of Professional Services' delivery. We define strategic initiatives, provide domain expertise, and oversee the development of high-quality, repeatable offerings that accelerate customer outcomes. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future. This is a customer-facing role and you will be required to travel to client locations and deliver professional services as needed. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Austin, TX, USA | Boston, MA, USA | Chicago, IL, USA | Herndon, VA, USA | Minneapolis, MN, USA | New York, NC, USA | San Diego, CA, USA | San Francisco, CA, USA | Seattle, WA, USA
US, MA, North Reading
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking Applied Science Interns and Co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects within robotics. Examples of projects include allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. As an Applied Science Intern/Co-op at Amazon Robotics, you will be working on one or more of our robotic technologies such as autonomous mobile robots, robot manipulators, and computer vision identification technologies. The intern/co-op project(s) and the internship/co-op location are determined by the team the student will be working on. Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, optimization and more. Learn more about Amazon Robotics: https://amazon.jobs/en/teams/amazon-robotics We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Seattle, WA, USA | Westborough, MA, USA
CA, BC, Vancouver
Amazon Web Services (AWS) is building a world-class marketing organization that drives awareness and customer engagement with the goal of educating developers, IT and line-of-business professionals, startups, partners, and executive decision makers about AWS services and solutions, their benefits, and differentiation. As the central data and science organization in AWS Marketing, the Data: Science and Engineering (D:SE) team builds measurement products, AI/ML models for targeting, and self-service insights capabilities for AWS Marketing to drive better measurement and personalization, improve data access and analytical self-service, and empower strategic data-driven decisions. We work globally as a central team and establish standards, benchmarks, and best practices for use throughout AWS Marketing. We are looking for a Principal Data Scientist with deep expertise in scaling measurement science, content ranking and rapid experimentation at scale, with strong interest in building scalable solutions in partnership with our engineering organization. You will lead strategic measurement science initiatives across AWS Marketing & Sales ranging anywhere between recommender engines, scaling experimentation and measurement science, real-time inference, and cross-channel orchestration. You are an hands-on innovator who can contribute to advancing Marketing measurement technology in a B2B environment, and push the limits on what’s scientifically possible with a razor sharp focus on measurable customer and business impact. You will work with recognized B2B Marketing Science and AI/ML experts to develop large-scale, high-performing measurement science models and AI/ML capabilities. We are at a pivotal moment in our organization where AI/ML and measurement velocity has reached an unseen momentum, and we need to scale fast in order to maintain it. Your work will be a key input into a few of our key business goals. You will advance the state of the art in measurement at scale. We are open to hiring candidates to work out of one of the following locations: Vancouver, BC, CAN
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities • Develop automated laboratory workflows. • Perform data QC, document results, and communicate to stakeholders. • Maintain updated understanding and knowledge of methods. • Identify and escalate equipment malfunctions; troubleshoot common errors. • Participate in the updating of protocols and database to accurately reflect the current practices. • Maintain equipment and instruments in good operating condition • Adapt to unexpected schedule changes and respond to emergency situations, as needed. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing (WFS) organization is on the front line of that mission by hiring the hourly fulfillment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon has created the Workforce Intelligence (WFI) team. This team will (re)invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team owns multi-layered research and program implementation to drive deep learning, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you. The Data Scientist will be responsible for creating cutting edge algorithms, predictive and prescriptive models as well as required data models to facilitate WFS at-scale warehouse associate hiring. This role acts as an internal consultant to the marketing, biz ops and candidate experience teams covering responsibilities such as at-scale hiring process improvement, analyzing large scale candidate/associate data and being strategic to providing best candidate hiring experience to WFS warehouse associate candidates. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Are 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. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a Applied Scientist to focus on large vision and manipulation machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes using machine learning to drive hardware movement. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. About the team This team invents and runs robots focused on grasping and packing items. These are typically 6-dof style robotic arms. Our work ranges from the long-term-research on basic science to deploying/supporting large production fleets handling billions of items per year. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon launched the Generative AI (GenAI) Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate enterprise innovation and success with Generative AI (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). Customers such as Highspot, Lonely Planet, Ryanair, and Twilio are engaging with the GAI Innovation Center to explore developing generative solutions. GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As a data scientist at GAIIC, you are proficient in designing and developing advanced Generative AI based solutions to solve diverse customer problems. You will be working with terabytes of text, images, and other types of data to solve real-world problems through Gen AI. You will be working closely with account teams and ML strategists to define the use case, and with other scientists and ML engineers on the team to design experiments, and find new ways to deliver value to the customer. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners. This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. About the team Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problem at Amazon scale? Are you excited by developing and productionizing machine learning, deep learning algorithms and leveraging tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diverse set of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Virtual Try On (VTO) at Amazon Fashion & Fitness is looking for an exceptional Applied Scientist to join us to build our next generation virtual try on experience. Our goal is to help customers evaluate how products will fit and flatter their unique self before they ship, transforming customers' shopping into a personalized journey of inspiration, discovery, and evaluation. In this role, you will be responsible for building scalable computer vision and machine learning (CVML) models, and automating their application and expansion to power customer-facing features. Key job responsibilities - Tackle ambiguous problems in Computer Vision and Machine Learning, and drive full life-cycle of CV/ML projects. - Build Computer Vision, Machine Learning and Generative AI models, perform proof-of-concept, experiment, optimize, and deploy your models into production. - Investigate and solve exciting and difficult challenges in Image Generation, 3D Computer Vision, Generative AI, Image Understanding and Deep Learning. - Run A/B experiments, gather data, and perform statistical tests. - Lead development and productionalization of CV, ML, and Gen AI models and algorithms by working across teams. Deliver end to end. - Act as a mentor to other scientists on the team. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, CA, Sunnyvale
At Amazon Fashion, we are obsessed with making Amazon Fashion the most loved fashion destinations globally. We're searching for Computer Vision pioneers who are passionate about technology, innovation, and customer experience, and who are enthusiastic about making a lasting impact on the industry. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey and change the world of eCommerce forever Key job responsibilities As a Applied Scientist, you will be at the forefront to define, own and drive the science that span multiple machine learning models and enabling multiple product/engineering teams and organizations. You will partner with product management and technical leadership to identify opportunities to innovate customer facing experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but more importantly influence strategy and outcomes across different Amazon organizations such as Search, Personalization and more. This role is inherently cross-functional and requires a strong ability to communicate, influence and earn the trust of software engineers, technical and business leadership. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA