Derek Chibuzor, who is pursuing a masters in electrical engineering with an emphasis in machine learning and data science at USC, is seen pointing to a poster while another person listens to his explanation.
Derek Chibuzor, who is pursuing a masters in electrical engineering with an emphasis in machine learning and data science, says he would eventually like to found a new business or startup.

USC SURE student develops prototype algorithm to help automate spacecraft docking

Derek Chibuzor utilized his SURE experience to gain "exposure to an aerospace research project in a professional research environment."

As a New Jersey native attending boarding school in Canada, Derek Chibuzor spent a lot of time on planes as a young student. The travel sparked an enduring interest in flight, and when he enrolled at the University of Southern California (USC), Chibuzor chose to major in aerospace engineering.

Chibuzor attended the prestigious Ridley College boarding school in Ontario, Canada, enrolling in an International Baccalaureate diploma program with a focus on STEM. After graduating from Ridley, he considered becoming a fighter pilot but ultimately chose to enroll in USC’s aerospace engineering program — in part because it would give him access to future internships at large aerospace companies.

His experience already has gone beyond the classroom. Through a unique summer fellowship in 2022, Chibuzor (who was then an undergrad) gained real-world experience conducting research that could help change how spacecraft dock with one another.

What impressed me most was his thirst for knowledge. He’s a student in aerospace but he wants to know about large language models, he wants to know about computer science — he really wants to know everything about everything.
Gérard Medioni

Chibuzor is one of dozens of students who have enrolled in the Amazon-sponsored Summer Undergraduate Research Experience (SURE). The fellowship provides students from historically underrepresented backgrounds with a unique research experience at top-tier universities including USC.

As a SURE fellow in 2022, Chibuzor spent the summer after his first year at USC’s Space Engineering Research Center working on a research project, with visits to Amazon. As part of the program, he was also mentored by Gérard Medioni, Amazon vice president and distinguished scientist, and emeritus professor at USC.

“What impressed me most was his thirst for knowledge,” Medioni said. “He’s a student in aerospace but he wants to know about large language models, he wants to know about computer science — he really wants to know everything about everything.”

Medioni, who previously served as the chair of the computer science department at USC, was able to offer Chibuzor advice on “a set of different options that he could follow while trying to get a dual curriculum of aerospace engineering and computer science.”

“I’ve always been passionate about aerospace and engineering. But I think what the SURE program helped me realize is that I’m also interested in computer science and computer engineering,” Chibuzor said.

Valuable research experience

During USC’s 2022 SURE program at the Space Engineering Research Center, which is part of the school’s Information Sciences Institute and affiliated with the university’s Viterbi School of Engineering, Chibuzor participated in a project called “CLING-ERS”. The CLING-ERS project goal was to develop an autonomous spacecraft docking solution for the International Space Station.

Chibuzor, who was later joined by two other interns to take on the ambitious challenge, was tasked with developing a computer vision algorithm.

“To achieve autonomous rendezvous and docking, each CLING-ERS device is equipped with an infrared camera and a set of four infrared LEDs,” Chibuzor explained. “By detecting the location and orientation of the IR LEDs attached to the opposing device, each CLING-ERS device is able to determine its position, attitude, and range with respect to the other — enabling the pair of to navigate toward one another and dock. To facilitate this IR LED detection, my team developed a computer vision algorithm using OpenCV.”

Derek Chibuzor. left, is seen standing in front of some posters that were part of a presentation for a project called “CLING-ERS” which had the goal of developing an autonomous spacecraft docking solution for the International Space Station.
During USC’s 2022 SURE program at the Space Engineering Research Center, Derek Chibuzor participated in a project called “CLING-ERS” with the goal was of developing an autonomous spacecraft docking solution.

The team utilized SimpleBlobDetector, a tool which can extract blobs — a region in an image that is distinct from the rest in terms of brightness or color. “When docking, the IR camera of each CLING-ERS device captures live video of the IR LEDs attached to the opposing device,” he noted. “Our algorithm could then continually analyze this live video to determine the position, attitude, and range of the four IR LEDs.

“This proved challenging for a few reasons, but one of the more interesting problems involved lens flare,” he continued. “As CLING-ERS approached to dock, lens flare caused the shape of the light emitted by the IR LEDs to distort from circular to rectangular — limiting the efficacy of the algorithm at close range. To remedy this, we developed a feature that dynamically tuned the detection constraints of the algorithm as docking progressed.”

Chibuzor and his team presented their research and prototype solution to the SURE faculty team and their peer interns in August of 2022.

Expanding STEM representation

Chibuzor’s experience as a USC SURE intern demonstrates the potential of exposing undergraduates from underrepresented groups to real-world scientific research and Amazon’s culture of innovation.

“Amazon’s SURE program at USC aims to fulfill the mission of diversifying the pipeline of students in STEM, focusing on an array of projects including machine learning, AI and other areas that Amazon is focused on,” said Andy Jones-Liang, associate director of Viterbi Academic Services at USC’s Viterbi School of Engineering and the school’s SURE program lead. “We had a smaller SURE program at USC before Amazon’s partnership, but thanks to Amazon’s gift and sponsorship, we’ve been able to triple the number of students enrolling in the program.”

While I've always been interested in computers and computer programming, SURE was really amazing because I got exposure to an aerospace research project in a professional research environment.
Derek Chibuzor

“SURE is about purposefully looking at the talent in a very broad section of our community and understanding both how can we can become more aware of that talent and also help those students realize that their talents are useful to companies like Amazon,” Medioni added.

Each year, USC faculty volunteer to host SURE students for the summer. Students apply for projects based on their personal and academic interests. USC faculty and Jones-Liang then work together to select students who match up well with faculty projects based on their backgrounds and research potential.

Accepted students work on their research projects for eight weeks and prepare a poster presentation for SURE faculty and peer interns. They also participate in weekly professional development events, join weekly lunch-and-learns to hear about graduate fellowship opportunities, and participate in weekend socials to build relationships, expand their networks, and explore the local area. The fellowship includes the opportunity to visit Amazon’s local offices and receive direct mentorship from an Amazonian.

“SURE, and Amazon’s involvement in it, helps address the broader societal goals of increasing representation when it comes to solving the STEM problems we're encountering in the world,” Jones-Liang said. “Students can see what a future in STEM might be like — whether in academia or industry.

“For many of these SURE students like Derek, this is typically their first or second research experience,” he continued. “So a lot of the program is designed to familiarize them with the research environment and give them exposure to a real-world research project.”

“While I've always been interested in computers and computer programming, SURE was really amazing because I got exposure to an aerospace research project in a professional research environment,” Chibuzor agreed.

SURE projects also help students understand the iterative, trial-and-error nature of research.

“Much of the program is about fostering that intrinsic motivation to want to learn and get over the hurdles that always present themselves when you’re innovating and doing something new,” said Jones-Liang.

As part of his SURE fellowship, Chibuzor also visited Amazon’s Culver City office — meeting employees, listening to guest speakers from both Amazon and academia, and immersing himself in the Amazon environment.

“I was a student myself and as a student, you have a very limited visibility on the workforce and the environment where you may find yourself,” Medioni observed. “SURE opens the windows on the experience at Amazon in a way that is useful to those students.”

In the summer of 2023, Chibuzor furthered his experience in aerospace engineering through a second internship with Northrop Grumman. Chibuzor was recently admitted to USC’s graduate engineering school to pursue a masters in electrical engineering with an emphasis in machine learning and data science. Longer term, he would like to exercise his entrepreneurial muscles and found a new business or startup.

“The SURE internship exposed me to a lot of computer science and computer engineering, sparking an interest for me to further that and create something of my own,” he said.

Research areas

Related content

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.
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.
GB, London
As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: Analyze large-scale datasets using structural econometric techniques to solve complex business challenges Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models Building datasets and performing data analysis at scale Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value Build and refine comprehensive datasets for in-depth structural economic analysis Present complex analytical findings to business leaders and stakeholders
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Design and execute model distillation strategies—distilling large frontier LLMs and VLMs into compact, production-grade models—that preserve multimodal reasoning capability while dramatically reducing serving latency, cost, and infrastructure footprint at billion-product catalog scale * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research