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 Suacode.ai 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, SuaCode.ai, 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.

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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.”

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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 SuaCode.ai 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.

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Amazon Leo is a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband network connectivity to unserved and underserved communities around the world. We are looking for an Applied Scientist to be a founding scientist on the Engineering and R\&D team within Leo Infrastructure and IP Security. The team defends the manufacturing lines, launch sites, and global ground infrastructure behind the constellation from the most sophisticated threat actors on the planet. These requirements create open scientific problems at the intersection of agentic AI, real-time stream processing, graph-based reasoning, and behavioral analytics. You will build the science behind a neurosymbolic reasoning platform and the models that detect the behavior of sophisticated threat actors. This is an R\&D role with a production mandate, where you define the problem rather than solve a pre-scoped one, and every model, detection, and agent workflow you build becomes the system Leo's security teams use to protect the constellation. #### Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities - Design and implement scalable, production-grade neurosymbolic systems that integrate symbolic reasoning over graph-based knowledge representations with LLM agents to deliver reliable, verifiable security outcomes. - Design and run reinforcement learning and fine-tuning pipelines (GRPO, PPO, DPO) to optimize language models for security reasoning, triage, and detection-authoring tasks. - Build behavioral and statistical models that detect threat actor behavior, and design the evaluation frameworks that measure model performance against that behavior before trusting a model in production. - Design and build multi-agent systems that autonomously triage, enrich, and contain security events, including the constrained reasoning, safety guardrails, and validation mechanisms that make automated decisions trustworthy at scale. - Own the end-to-end science lifecycle, from research and experimentation through production deployment, defining the metrics that measure system performance and real-world security impact. - Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, and shape the scientific agenda and research culture from day one. A day in the life You will move between research and production in the same week: framing an ambiguous security problem as a scientific question, prototyping an approach, and partnering with software engineers to ship it as a capability the platform runs continuously. Security engineers on your team translate threat intelligence into the adversary behaviors that matter; you build the models that detect those behaviors and evaluate model performance against them. You will obsess over the two latencies that define the platforms, the time from event to detection and the time from detection to containment action, and design agents and detections that drive both down. You will backtest candidate detections against retained telemetry, review evaluation results before a model or agent capability graduates to automated execution, and deliver scientific artifacts that ship. About the team Leo Infrastructure and IP Security protects the people, facilities, hardware, and supply chain behind a global satellite constellation. The Engineering and R\&D team within this organization builds the platforms and tooling the security pillar teams operate on, moving security operations from manual triage to correlation-based detection, automated response, and agentic AI. The team is composed of applied scientists, software engineers, and security engineers working across physical and digital security domains. #### Inclusive Team Culture In Amazon Security, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. #### Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
US, WA, Seattle
Ever wonder how you can keep the world’s largest selection also the world’s safest and legally compliant selection? Then come join a team with the charter to monitor and classify the billions of items in the Amazon catalog to ensure compliance with various legal regulations. The Classification and Policy Platform team is looking for Sr. Applied Scientists to build technology to automatically monitor the billions of products on the Amazon platform. The software and processes built by this team are a critical component of building a catalog that our customers trust. You will have an opportunity to work with cutting edge machine learning algorithms on large datasets. You will need to build Amazon scale applications running on Amazon Cloud that both leverage and create new technologies to process large volumes of data that derive patterns and conclusions from the data. We are looking for highly motivated applied scientists and engineers interested in delivering the next level of innovation to product search for Amazon. As an Applied Scientist on the CPP team, you will be responsible for working across backend, client, business development, and data engineering teams to coordinate deep-dives, inform roadmaps, visualize metrics, and create predictive models to determine how we can best serve our customers. Responsibilities include: - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching and ranking problems, including filtering, new content indexing, and apply document understanding - Conducting and coordinating process development leading to improved and streamlined processes for model development. Strong customer focus is essential - Working closely with Product Managers to expand depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps - Providing technical and scientific guidance to your team members - Communicating effectively with senior management as well as with colleagues from science, engineering, and business backgrounds - Being a cultural leader that ensures teams are collecting, understanding, and using data to inform every decision that impacts our customers The successful candidate will have an established background in developing customer-facing experiences, a strong technical ability, a start-up mentality, excellent project management skills, and great communication skills. 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.