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.

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 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.

Related content

ES, B, Barcelona
Are you interested in defining the science strategy that enables Amazon to market to millions of customers based on their lifecycle needs rather than one-size-fits-all campaigns? We are seeking a Applied Scientist to lead the science strategy for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing analytics and science) team. The position is open to candidates in Amsterdam and Barcelona. In this role, you will own the end-to-end science approach that enables EU marketing to shift from broad, generic campaigns to targeted, cohort-based marketing that changes customer behavior. This is a high-ambiguity, high-impact role where you will define what problems are worth solving, build the science foundation from scratch, and influence senior business leaders on marketing strategy. You will work directly with Business Directors and channel leaders to solve critical business problems: how do we win back customers lost to competitors, convert Young Adults to Prime, and optimize marketing spend by de-averaging across customer cohorts. Key job responsibilities Science Strategy & Leadership: 1. Own the end-to-end science strategy for lifecycle marketing, defining the roadmap across audience targeting, behavioral modeling, and measurement 2. Navigate high ambiguity in defining customer journey frameworks and behavioral models – our most challenging science problem with no established playbook 3. Lead strategic discussions with business leaders translating business needs into science solutions and building trust across business and tech partners 4. Mentor and guide a team of 2-3 scientists and BIEs on technical execution while contributing hands-on to the hardest problems Advanced Customer Behavior Modeling: 1. Build sophisticated propensity models identifying customer cohorts based on lifecycle stage and complex behavioral patterns (e.g., Bargain hunters, Young adults Prime prospects) 2. Define customer journey frameworks using advanced techniques (Hidden Markov Models, sequential decision-making) to model how customers transition across lifecycle stages 3. Identify which customer behaviors and triggers drive lifecycle progression and what messaging/levers are most effective for each cohort 4. Integrate 1P behavioral data with 2P survey insights to create rich, actionable audience definitions Measurement & Cross-Workstream Integration: 1. Partner with measurement scientist to design experiments (RCTs) that isolate audience targeting effects from creative effects 2. Ensure audience definitions, journey models, and measurement frameworks work coherently across Meta, LiveRamp, and owned channels 3. Establish feedback loops connecting measurement insights back to model improvements About the team The PRIMAS (Prime & Marketing Analytics and Science) is the team that support the science & analytics needs of the EU Prime and Marketing organization, an org that supports the Prime and Marketing programs in European marketplaces and comprises 250-300 employees. The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques
ES, M, Madrid
At Amazon, we are committed to being the Earth's most customer-centric company. The European International Technology group (EU INTech) owns the enhancement and delivery of Amazon's engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects. You will be joining the Tamale team to work on Haul. As part of EU INTech and Haul, Tamale strives to create a discovery-driven shopping experience using challenging machine learning and ranking solutions. You will be exposed to large-scale recommendation systems, multi-objective optimization, and state-of-the-art deep learning architectures, and you'll be part of a key effort to improve our customers' browsing experience by building next-generation ranking models for Amazon Haul's endless scroll experience. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading ranking solutions. We strongly value your hard work and obsession to solve complex problems on behalf of Amazon customers. Key job responsibilities We look for applied scientists who possess a wide variety of skills. As the successful applicant for this role, you will work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to optimize multi-objective ranking, improve discovery engagement through contextual signals, and scale ranking systems across multiple marketplaces. You will work with business leaders, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed ranking services. You will be part of a team of scientists and engineers working on solving ranking and personalization challenges at scale. You will be able to influence the scientific roadmap of the team, setting the standards for scientific excellence. You will be working with state-of-the-art architectures and real-time feature serving systems. Your work will improve the experience of millions of daily customers using Amazon Haul worldwide. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process!
IN, HR, Gurugram
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for International Emerging Stores (IES). Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the International Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions across International Emerging Store (India, MENA, Far-East, LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.