George Boateng is seen sitting on a chair on a stage while speaking on a panel about his SuaCode work at the African Union’s  2019 Innovating Education in Africa Expo in Gaborone, Botswana.
George Boateng speaking on a panel about his SuaCode work at the African Union’s 2019 Innovating Education in Africa Expo in Gaborone, Botswana.
Helge Tollefsen/African Union

"An accidental project born out of our need to innovate”

Former Amazon intern George Boateng is using machine learning and mobile tech to bridge Africa’s digital divide.

Throughout his short but impressive journey as an engineer and social entrepreneur, George Boateng has seen solutions in scenarios where many people see problems.

While attending boarding school in his home country of Ghana, his fellow students’ clothes were continuously stolen while hanging up to dry. In response, he developed a portable electric dryer.

“I've always really been interested in science, technology, and engineering, and in building things,” said Boateng, 29. “When I was a young boy, my family would travel to visit my grandmother. I was fascinated by her encyclopedias, which she let me take with me so I could do science experiments at home.”

At Dartmouth College, where Boateng earned a bachelor’s degree in computer science, a master’s in computer engineering, and was an E.E. Just STEM Scholar and E.E. Just Graduate Fellow, he teamed up with friends to create the Nsesa Foundation, a nonprofit committed to democratizing STEM education across sub-Saharan Africa. Nsesa teaches young people engineering and computer programming skills to help close STEM education and employment gaps in a place where, according to the World Economic Forum, less than 1% of children finish school with basic coding skills.

George Boateng talks at the 2020 Africa Summit at Princeton

“My cofounders and I started Nsesa to take Dartmouth’s popular introductory engineering course back home to Ghana,” Boateng explained. “I was amazed by how students, most of whom had not taken any advanced engineering courses, could go through a design and innovation process and actually build solutions to real-life problems and start companies.”

In 2013, he created a modified version of the course called Project iSWEST, a three-week innovation bootcamp in which high school and university students in Ghana could learn coding and innovation skills. When the program’s donated laptop computers had all broken down four years later, Boateng and his colleagues redesigned the eight-week, Java-based training program for devices all of the participants had: smartphones.

“SuaCode was truly an accidental project born out of our need to innovate around a lack of laptops,” said Boateng. That accidental project led MIT Technology Review to recently name Boateng one of its “35 Innovators Under 35.”

SuaCode teaches young students in Africa to code using Android devices and a bilingual (English and French) AI-powered teaching assistant, Kwame, named after Ghana’s first president, Kwame Nkrumah. After four successful pilots between 2018 and 2020, Boateng and his co-founder launched a startup,, to turn the program into a mobile app for greater scale and impact.

To date, SuaCode has introduced more than 2,000 learners from 42 African countries to the fundamentals of software. Boateng and his team are currently developing additional courses and partnering with universities across Africa to host and deliver programming through the SuaCode platform.

Related content
Scientists discuss the challenges in developing a system that can accurately estimate body fat percentage and create personalized 3D avatars of users from smartphone photos.

Boateng’s thirst for problem-solving also attracted him to an Alexa AI internship opportunity he saw online in 2020. “The Amazon Halo Band had just been released,” Boateng said. Amazon Halo is a health and wellness membership that integrates with a Halo device to help users manage their overall health. His research focused on Halo Tone, which analyzes qualities of voice, such as energy and positivity, to help members become more aware of how they may sound to others.

“The opportunity to work with the team of applied scientists that developed this first-of-its-kind technology was exciting to me,” he said.

For four months in 2021, Boateng worked with the Cambridge, Mass.-based Alexa AI team on one of the most challenging tasks in computational linguistics — sarcasm detection — with a focus on conversational speech. “Sarcasm can be ambiguous both to humans and to machines,” said Boateng, who completed the internship remotely from Zurich, where he is a doctoral candidate at ETH Zurich. “For example, if someone says ‘I love being ignored’, an emotion recognition system might think the statement is positive because of the use of ‘love’. But once you recognize sarcasm, you can infer this is actually a negative statement.”

Related content
Scientists updated the system to accurately measure body fat percentage and create personalized 3D models even if there’s not enough room to take a full-body photo.

The team took an experimental approach to sarcasm detection with the goal of improving Amazon Halo Tone features, conducting text and speech analyses on hundreds of episodes of two popular TV sitcoms — “Friends” and “The Big Bang Theory”.

“Before diving into this machine learning problem, our first step was to correctly define sarcasm,” Boateng said. “Our approach was grounded in linguistics theory and an empirical understanding of sarcastic utterances to comprehensively address sarcasm detection in conversational speech.”

Boateng and his colleagues developed a taxonomy of incongruity and expression in sarcastic utterances and performed systematic error analysis towards the goal of sarcasm detection. A paper is currently in the works. “We didn’t completely solve sarcasm detection,” Boateng wrote on LinkedIn. “But we have taken a giant step towards that goal.”

George Boateng presenting his PhD research at the second Black in AI workshop at NeurIPS 2018 in Montréal, Canada
George Boateng presenting his PhD research at the second Black in AI workshop at NeurIPS 2018 in Montréal, Canada.
George Boateng

During the internship, Boateng sat in on weekly team meetings and welcomed feedback on his writing and problem-solving approaches from senior scientists. “It was really a big learning experience to understand Amazon’s ‘bias for action’ and ‘customer obsession’ principles,” he said. “I learned that you can’t spend too much time thinking about ways to approach a problem, you have to experiment and deliver results.”

“Alexa attracts top talent in machine learning and speech, due to opportunity to work on cutting-edge applied research,” said Viktor Rozgic, an Alexa principal applied scientist who was Boateng’s manager. “George’s background in developing emotion detection solutions for mobile and data collection design, as well as his ability to handle ambiguity, were very valuable on the project. We were impressed by his versatility, in particular his previous experience working on emotion recognition, mobile applications, and designing data collections.”

Boateng recommends an Amazon science internship for students motivated to tackle “real-world” problems without shying away from uncertainty.

“That’s what really drew me to Amazon,” he said. “A lot of times if you come from a technical background, your focus tends to be theoretical, publishing papers and presenting at conferences. But at Amazon, even though the work is technically rigorous, it’s always linked to real-world applications customers use.

“The key,” Boateng added, “is to not be scared to embrace big, ambiguous challenges.”

In addition to his PhD program at ETH Zurich, where he’s working on multi-modal emotion detection using sensor data from smartphones and smartwatches, Boateng is currently a visiting researcher at the University of Cambridge, where he’s exploring collaborations on AI-powered mobile health research. He remains focused on building while exploring mobile and wearable technologies in pervasive health.

“I’m passionate about using technology to help people live healthier lives,” said Boateng, who plans to pursue a postdoctoral research fellowship and hopes to become a professor. “I’m grateful for the opportunity to intern at Amazon. All of the lessons I learned will serve me well in the next chapter of my career and life.”

Amazon is looking for science interns around the world, click the button below to browse and apply for the latest open positions.

Related content

US, NY, New York
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at
FR, Clichy
The role can be based in any of our EU offices. Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth’s most customer-centric company” makes the customer fulfillment business bigger and more complex with each passing year. The EU SC Science Optimization team is looking for a Science leader to tackle complex and ambiguous forecasting and optimization problems for our EU fulfillment network. The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Statistics, Econometrics, Operations Research and Machine Learning models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with Supply Chain Optimization Technology (SCOT) teams, who own the systems and the inputs we rely on to plan our networks, the worldwide scientific community, and with our internal EU stakeholders within Supply Chain, Transportation, Store and Finance. The ideal candidate has a well-rounded-technical/science background as well as a history of leading large projects end-to-end, and is comfortable in developing long term research strategy while ensuring the delivery of incremental results in an ever-changing operational environment. As a Sr. Science Manager, you will lead and grow a high-performing team of data and research scientists, technical program managers and business intelligence engineers. You will partner with operations, finance, store, science and engineering leadership to identify opportunities to drive efficiency improvement in our Fulfillment Center network flows via optimization and scalable execution. As a science leader, you will not only develop optimization solutions, but also influence strategy and outcomes across multiple partner science teams such as forecasting, transportation network design, or modelling teams. You will identify new areas of investment and research and work to align roadmaps to deliver on these opportunities. This role is inherently cross-functional and requires an ability to communicate, influence and earn the trust of science, technical, operations and business leadership.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at Key job responsibilities Estimate econometric models using large datasets. Must know SQL and Matlab.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
US, WA, Seattle
Amazon Prime Video is changing the way millions of customers enjoy digital content. Prime Video delivers premium content to customers through purchase and rental of movies and TV shows, unlimited on-demand streaming through Amazon Prime subscriptions, add-on channels like Showtime and HBO, and live concerts and sporting events like NFL Thursday Night Football. In total, Prime Video offers nearly 200,000 titles and is available across a wide variety of platforms, including PCs and Macs, Android and iOS mobile devices, Fire Tablets and Fire TV, Smart TVs, game consoles, Blu-ray players, set-top-boxes, and video-enabled Alexa devices. Amazon believes so strongly in the future of video that we've launched our own Amazon Studios to produce original movies and TV shows, many of which have already earned critical acclaim and top awards, including Oscars, Emmys and Golden Globes. The Global Consumer Engagement team within Amazon Prime Video builds product and technology solutions that drive customer activation and engagement across all our supported devices and global footprint. We obsess over finding effective, programmatic and scalable ways to reach customers via a broad portfolio of both in-app and out-of-app experiences. We would love to have you join us to build models that can classify and detect content available on Prime Video. We need you to analyze the video, audio and textual signal streams and improve state-of-art solutions while being scalable to Amazon size data. We need to solve problems across many cultures and languages, working alongside an operations team generating labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art. As a member of our team, you will apply your deep knowledge of Computer Vision and Machine Learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on addressing fundamental computer vision models like video understanding and video summarization in addition to building appropriate large scale datasets. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions. We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.
US, CA, Palo Alto
Join a team working on cutting-edge science to innovate search experiences for Amazon shoppers! Amazon Search helps customers shop with ease, confidence and delight WW. We aim to transform Search from an information retrieval engine to a shopping engine. In this role, you will build models to generate and recommend search queries that can help customers fulfill their shopping missions, reduce search efforts and let them explore and discover new products. You will also build models and applications that will increase customer awareness of related products and product attributes that might be best suited to fulfill the customer needs. Key job responsibilities On a day-to-day basis, you will: Design, develop, and evaluate highly innovative, scalable models and algorithms; Design and execute experiments to determine the impact of your models and algorithms; Work with product and software engineering teams to manage the integration of successful models and algorithms in complex, real-time production systems at very large scale; Share knowledge and research outcomes via internal and external conferences and journal publications; Project manage cross-functional Machine Learning initiatives. About the team The mission of Search Assistance is to improve search feature by reducing customers’ effort to search. We achieve this through three customer-facing features: Autocomplete, Spelling Correction and Related Searches. The core capability behind the three features is backend service Query Recommendation.
US, CA, Palo Alto
Amazon is investing heavily in building a world class advertising business and we are responsible for 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. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and 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 Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning (ML) 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 energetic, entrepreneurial, and self-driven science leaders to join the team. Key job responsibilities As a Principal Applied Scientist in the team, you will: Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our ML innovations to the broader internal & external scientific community.
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) 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!"?
US, CA, Santa Clara
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: * Hardware-informed efficient model architecture, training objective and curriculum design * Distributed training, accelerated optimization methods * Continual learning, multi-task/meta learning * Reasoning, interactive learning, reinforcement learning * Robustness, privacy, model watermarking * Model compression, distillation, pruning, sparsification, quantization About Us Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 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 14 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 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.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.