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, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes, and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science team builds the machine learning (ML) systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value (LTV) frameworks, and the experimentation infrastructure that validates whether pricing changes actually work. We're looking for a Research Scientist to own causal inference at the intersection of ML and pricing experimentation. Our team has identified a real gap: the methodological bridge between econometric analysis and production-scale ML pipelines needs a practitioner who lives in both worlds. You'll build conditional average treatment effect (CATE) estimation models, design analysis workflows for pricing experiments, and develop reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. If you're a scientist who wants to tackle hard causal identification problems in a high-stakes production environment, and who finds satisfaction in making rigorous methods accessible to a broader team, this role is for you. Key job responsibilities - Design, train, evaluate, and deploy end-to-end causal ML pipelines for pricing use cases, including CATE estimation models and heterogeneous treatment effect frameworks. - Serve as the team's causal ML methodology expert — defining identification strategies, model selection criteria, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. - Contribute causal analysis methodology to pricing A/B test post-analysis and build reusable tooling that economists and other non-ML scientists can adopt without requiring deep ML expertise. - Assess emerging causal inference methods (e.g., synthetic difference-in-differences, generalized random forests, causal representation learning) for applicability to Amazon's pricing context, and author internal methodology proposals for adoption. - Partner with senior economists on identification strategy and causal assumptions, collaborate with software and data engineering partners on production deployment, and align with product managers on experiment design requirements. A day in the life Your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given week you might investigate an optimization anomaly in simulation and trace it to an unmodeled market dynamic, then design an offline evaluation framework to benchmark competing approaches before committing to online testing. You'll collaborate with senior economists on identification strategy for a pricing experiment, present a science proposal for incorporating a new competitiveness signal into an optimization system, or work with the experimentation platform team on randomization design. You'll also have opportunities to write up novel scientific findings for submission to venues such as KDD, NeurIPS, or the ACM Conference on Economics and Computation. About the team The P2 Optimization Science team sits at the center of Amazon's pricing decisions. We build the ML systems, causal inference pipelines, and experimentation infrastructure that determine how prices are set across Amazon's global catalog. Our team brings together research scientists, economists, and engineers who collaborate closely to turn rigorous methodology into production-quality systems with measurable business impact. We value scientific rigor, clear communication across disciplines, and a culture where every team member's work is designed to be extensible and useful beyond their own project. If you want your science to directly influence how hundreds of millions of customers experience Amazon, this is the team to join.
US, CA, San Francisco
Amazon’s Center for Quantum Computing (CQC) is building the next generation of quantum computing hardware. The Center for Quantum Computing (CQC) is our longest-horizon bet and operates at the intersection of hardware and physics. The goal is a quantum computer that could solve in hours what today's most robust machines would take thousands of years to compute, unlocking breakthroughs in myriad of areas ranging from advanced materials for batteries to cryptography and fundamentally-secure data sharing protocols. This program involves R&D in device theory, materials science, nano-scale fabrication, superconducting circuit design, cryogenic refrigeration, and quantum algorithms and applications. Currently, the quantum hardware team is focused on improving the underlying chip fabrication of the quantum processor and the scaling of the control system needed to run large-scale quantum circuits. As part of this effort, the team is expanding its chip fabrication capabilities through the construction of a new cleanroom facility dedicated to superconducting quantum chip fabrication, and working with strategic partners to develop scaled versions of the control electronics, I/O wiring, and cryogenic refrigeratation needed for utility-scale quantum computers. We operate with a startup-like pace inside one of the world's most capable technology organizations, combining the urgency of a competitive frontier with the resources and infrastructure of pan Amazon. We are seeking an Applied Scientist III to join our team and drive the design, construction, and operation of quantum processors. You will work alongside world-class physicists and engineers to push the frontier of quantum computing — from laser system design and atom trapping to high-fidelity gate execution and quantum error correction. This is a rare opportunity to be part of shaping a technology that could define the next era of computing. Key job responsibilities - Design, build, and operate quantum computing experiments, including optical trapping arrays, laser cooling and state-preparation systems, and high-fidelity gate-driving optics - Develop and optimize two-qubit entangling gates using interactions, targeting fidelities at or beyond the current state of the art (>99.5%) - Implement and characterize erasure-detection protocols leveraging the metastable-state structure of ¹⁷¹Yb to achieve hardware-native erasure conversion rates >90% - Contribute to quantum error correction experiments, including surface codes and quantum low-density parity-check (qLDPC) codes, and work toward demonstrating error suppression factors (Λ) well beyond current published records - Design and improve atom-array loading, rearrangement, and continuous replenishment techniques to support registers of thousands to tens of thousands of atoms - Develop control software and calibration routines for high-speed experimental sequences, including real-time feedback and adaptive protocols - Analyze experimental data, build physical models to explain observed behavior, and propose and execute improvements to system performance - Collaborate with theory, software, and hardware engineering teams to co-design quantum architectures, error-correcting codes, and decoder systems - Publish results in leading scientific journals and present at top conferences to maintain Amazon's visibility in the quantum research community
CH, Zurich
RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one. Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies. By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills. We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics. Key job responsibilities Develop imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks. Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms. Streamline the workflow to efficiently expand the imitation learning dataset with new tasks. Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data. Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints. Build, lead and mentor an exceptional team of software engineers. Provide expert guidance to product managers and executives for strategic decision-making. Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.
US, NY, New York
The Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) organization of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. This Principal Applied Scientist leads the technical vision and execution for Amazon Advertising's performance measurement. Our measurement products and tools help customers understand the holistic impact of their spend on Amazon Ads and optimize their portfolios. This is a complex, multi-disciplinary scientific space spanning machine learning, causal inference, statistics and experimentation, natural language processing, and generative AI. As a GenAI-first organization, we build foundational and agentic models that power advertiser use cases across Ads, while empowering our Applied Scientists to directly build and ship products. You will guide a team of scientists with expertise across different domains to come together and innovate on some of the hardest problems in advertising, made more challenging by evolving privacy regulations and technological changes. In measuring the comprehensive impact of advertising, key challenges you will tackle include capturing impact: 1) both for identified and increasingly unidentified web traffic in light of an evolving global privacy landscape; 2) for outcomes across retailers, including both within and beyond Amazon; and 3) for both short- and long-term impacts of advertising. Key job responsibilities * Driving the scientific vision of the teams in your organization and you advise and influence its technical leadership on performance measurement models and products. * Identifying, tackling, and proposing innovative solutions to intrinsically hard, previously unsolved problems. You solve problems pragmatically, applying judgment and experience to balance trade-offs between competing interests. * Bringing clarity to complex problems, probing assumptions, illuminating pitfalls, fostering shared understanding, and guiding towards effective solutions. * Serving and being recognized by internal and external peers as a thought leader in modeled measurement in Ads. You will engage routinely with the broader scientific community, e.g. as a reviewer, program committee member, workshop organizer, or chair at peer-reviewed internal/external conferences. * Leading by example, with your papers, analyses, and code setting the standard in your organization for scientific engineering excellence. * Leading science and design reviews, aligning teams across your organization around coherent algorithmic strategies. * Influencing your team’s science and business strategy by driving one or more team roadmaps contributing to the organization’s roadmap and taking responsibility for some organizational goals. You drive multiple new product features from inception to production launch. * Guiding the career development of others, actively mentoring and educating the larger applied science community on trends, technologies, and best practices. You drive the documentation of scientific innovation. You attract bar-raising talent from academia and industry. * Staying abreast of academic and industry trends, knowing the state of the art, and building on top of existing solutions instead of reinventing when possible. About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
US, CA, Sunnyvale
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building robotic systems that combine frontier AI, sophisticated control, and advanced mechanical design to create adaptable automation capable of working safely alongside humans in dynamic environments. This is an opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. We are seeking an Applied Scientist to develop tactile- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning. To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from simulation onto real hardware. That means being comfortable working against real physical constraints and treating sim-to-real as a core research problem rather than optimizing for benchmark numbers alone. You will work across tactile signal processing, contact-rich manipulation, sim-to-real transfer, and multi-modal sensor fusion to build robust solutions for autonomous grasping, dexterous re-orientation, and fine motor control. You will bring the desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team. Key job responsibilities - Design and train manipulation policies that leverage tactile and force feedback for dexterous grasping, in-hand manipulation, and contact-rich tasks, using reinforcement learning, imitation learning, or hybrid approaches. - Drive sim-to-real transfer, closing the gap between simulated and physical performance on real robotic hands. - Shape objectives and control strategies with the hardware in mind, accounting for how motors and actuators actually behave: transmission ratios, torque versus power, backdrivability, and thermal limits. - Build simulation-based and on-robot evaluation frameworks, with benchmarks and metrics that make tactile perception and policy performance systematically comparable across iterations. - Own scientific and technical projects within the tactile sensing and manipulation workstreams, driving from research concept through deployment on physical robotic systems — and, at the senior level, set direction across a workstream. - Collaborate with hardware, mechanical design, firmware, and controls teams — and with partner applied science organizations — to translate research advances into deployable robotic capabilities, and to inform sensor and actuator design decisions with policy-level requirements. - Publish at top-tier venues and build collaborations with the external research community. - Contribute to a strong scientific bar on the team through code and design review, and mentor engineers and interns working on real-world manipulation and sensing problems. A day in the life Your morning might start with a standup alongside hardware and controls engineers reviewing overnight sim-to-real training runs on a multi-finger gripper, debugging why a grasp policy that worked in simulation is slipping on a real sensor array. Mid-morning you join the weekly tactile sensing workstream to align on sensor integration milestones and share early results from a contact-state estimator you have been prototyping. After lunch you spend a focused block iterating on a reinforcement learning reward formulation, testing variations in simulation before queuing runs on the cluster. Later you pair with a mechanical engineer to review sensor placement trade-offs on an upcoming gripper revision, then wrap the day by drafting a short experiment write-up and sketching next steps for a conference submission. No two days look exactly the same: one week you may be collecting teleoperated demonstrations on the physical robot to seed an imitation learning pipeline; the next you could be deep in a codebase refactor to support a new tactile modality. Throughout, you balance hands-on research — writing algorithms, running experiments, analyzing data — with cross-team collaboration and mentoring. The common thread is moving from research insight to working capability on real hardware, with a tight feedback loop between simulation and the physical world. About the team Our is developing next-generation manipulation capabilities for highly dexterous multi-fingered grippers and hands. Our work spans tactile sensor development, manipulation policy learning, and the tight integration of sensing hardware with intelligent control software. We are building systems that can feel, adapt, and manipulate with human-level dexterity. You will join a team working at the frontier of tactile sensing and contact-rich manipulation, helping define how robots perceive and interact with objects through touch. Because we build the hands as well as the policies that drive them, you will have a direct line to the sensor, actuator, and mechanism designs your algorithms depend on — and real influence over how they evolve. This is an opportunity to solve hard problems across perception, learning, and physical interaction alongside a team of scientists and engineers building toward real-world deployment.
US, WA, Redmond
Amazon Leo is building a low Earth orbit satellite broadband network to deliver fast, affordable connectivity to customers around the world, including those underserved by traditional infrastructure. Operating a live constellation and its ground network at scale means service disruptions are an inevitable reality, and how quickly we detect them, understand them, and prevent them from recurring defines the trust we earn as a connectivity provider. We are seeking a talented Senior Applied Scientist to build the next generation of observability and availability science for the Leo network. Leo already detects outages, notifies affected customers, and restores service. The frontier is prediction and prevention: reasoning over complex network and ground-segment telemetry to autonomously detect anomalies before they reach customers, attribute root cause automatically across a sprawling event surface, and rationalize a large, still-maturing metric set so responders trust what the system tells them. In this role you will work backwards from the needs of customers and operations teams. You will own the scientific direction end to end, partnering closely with network engineers, the Network Operations Center, and the Incident Management team, and the Software and Networking orginization to translate a long-term science vision into concrete research and delivery roadmaps. 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 * Own the observability and availability science end to end, from long-term vision to research roadmap to production delivery, working backwards from the needs of customers, the NOC, and network engineering. * Develop time-series and graph-based models with dynamic baselining that detect network and ground-segment anomalies before they cross the High Severity threshold, enabling proactive investigation rather than reactive response. * Reason over complex, multi-system telemetry to attribute cause automatically during live HSEs and LSEs, so Call Leaders and engineers spend time mitigating, not hunting. * Provide the science behind the network's bucketed availability decomposition (geometric and satellite coverage, ground and network segments, customer terminals, payload, and planning), attributing every movement to a true cause rather than noise, so leadership steers on signal and engineering effort lands where it actually moves availability and throughput. * Deliver the models, analyses, and metrics that feed the daily system readiness and validation review consumed by senior leadership and product and engineering, turning availability and throughput data into a defensible, evidence-based read on progress against GA entry criteria. * Own the recurring, fleet-wide "hot topic" investigations that span ASIC, firmware, payload, network, and planning, separating correlated symptoms from true root cause across thousands of nodes, quantifying how much availability or throughput each is costing, and answering whether it is a launch-blocking risk and whether it is improving. * Deliver alert correlation and deduplication, false-positive reduction, and metric-quality scoring so responders trust what the system tells them, directly supporting a large, still-maturing KPI set. * Tie your science to the unit the org actually measures: reduce detection and attribution time, raise availability against the network's bar, and increase the share of events prevented before customer impact. * Partner day to day with the Network Operations Center, Network Engineering, and the Incident Management (Call Leader) program; sit on live HSE/LSE bridges as the science partner and turn event learnings into model improvements. * Feed COEs and RCAs back into detection and attribution so recurring signatures are caught earlier, and help design and score gamedays that stress-test both the network and your models against real, closed events. * Build and deploy production pipelines on AWS (e.g., SageMaker, Bedrock, Glue) with model validation, monitoring, drift detection, and explainability, because operations teams must be able to trust and act on model output under pressure. * Set data-science standards, mentor scientists, and drive best practices across the Leo networking organization as the science function scales. * Produce explainable output and written narratives that let responders and leaders three or more levels up act on your models during and between events.
US, TX, Austin
The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Data Scientist to help own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, supporting proactive incident prevention - Apply econometric and statistical methods to measure the impact of operational interventions and inform resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Contribute to data quality monitoring frameworks and help maintain standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate analytical insights into actionable recommendations - Communicate findings, model performance, and recommendations to stakeholders and senior leaders through written documents and presentations - Contribute to the team's science roadmap, identifying opportunities to apply ML/AI (including Generative AI) to operational challenges A day in the life Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan
CN, 31, Shanghai
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands. Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact. The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases. Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.