Donato Crisostomi is seen sitting inside with some potted plants in the background
Donato Crisostomi, a PhD student at Sapienza Università di Roma, recently accepted another six-month internship at TEN Search, Amazon’s Luxembourg location.

“I didn’t imagine I could grow and learn so much”

Donato Crisostomi talks about how his mother helped spark a love of knowledge that led him to two science internships at Amazon.

From an early age, Donato Crisostomi was fascinated with mathematics and philosophy. That interest was nurtured by his mother — and eventually helped him land two science internships with Amazon.

As a child growing up in Civitavecchia, a small coastal city near Rome, he enjoyed exploring his natural environment.

Amazon science intenships
This year’s class includes applied science, research science, and data science interns.

“I spent all my time running around in nature poking at leaves, animals, all sorts of things. Each of these encounters filled my mind with questions,” he recalled.

A curious young boy, he looked to his mother for answers.

“She always did her best to answer my questions,” he remembered. While his mother did not have the opportunity to pursue higher education, Crisostomi felt she would have made a great engineer or scientist if given the chance.

“She understood, and probably still does, how an electronic or mechanical device works better than anyone in the house, including me,” he said.

His mother’s enthusiasm helped spark a passion for learning that has served him well.

Forging his own path

As a first-generation college student, Crisostomi forged his own path toward higher education. Yet it took time for Crisostomi to discover his own abilities. After dropping out of high school and spending a year on a working holiday in Australia, he returned to Italy. He graduated high school, spent a year in the Italian army, and gained the clarity he needed about his long-term goals: He wanted to study computer science.

Computer science provided Crisostomi a platform to indulge his passion for knowledge.

“Computer science has deep philosophical roots,” he stated. “But I was most interested in the ones regarding artificial intelligence.”

Crisostomi went on to earn bachelor’s and master’s degrees in computer science from Sapienza Università di Roma. While finishing his master’s in May 2021, Crisostomi joined the Alexa Natural Language Understanding organization as an intern at Amazon’s Turin office.

Related content
How he parlayed an internship to land an expanded role at Amazon while pursuing his master’s degree.

He had been following Amazon on LinkedIn since his first year in college as his goal was to conduct applied research.

“I was trying to choose between the dynamic environment of a new start-up or a larger, more consolidated company,” he said. “From the information I gathered online, Amazon’s ‘Day One’ philosophy was a perfect compromise.”

The thought of working at Amazon appeared daunting at first, but Crisostomi’s experience as an intern eased his anxiety.

“I was afraid that I would find a cold environment as it was my first experience in such a huge corporation,” he recalled. “I had some expectations coming from movies I had seen that I would have to talk in front of hundreds of people who would be staring at me and judging me.”

Instead, he found a welcoming and encouraging environment, particularly when it came to his manager Davide Bernardi, a data science manager at Alexa AI Natural Understanding.

“It was really lovely working with Davide and the team at Amazon,” Crisostomi said. “Everyone was so friendly; Davide was so nice, and encouraged me to share my ideas.”

Thinking big

As part of Bernardi’s team, Crisostomi worked with research scientists and developers to automate the creation of artificial cross-lingual datasets to test a neural model against complex multilingual phenomena.

Related content
Today she's helping Amazon to better formulate how to more efficiently transport packages through the middle mile of its complex delivery network.

“We wanted to design a neural model which could recognize code mixed utterances,” Crisostomi explained. Code mixing refers to the use of multiple languages in the same utterance – common among bilingual and multilingual speakers. The challenge: have the neural model understand different languages and dialects in the same utterances.

Crisostomi had an idea to make the neural model’s multilingual capacity more efficient by reducing repeated computation during training and removing duplication at the batch level while keeping the data distribution perceived by the model untouched.

Bernardi said he noticed how quickly Crisostomi became proficient in the tools, platforms, techniques, and models.

“We had him work on a big goal when we were already in the middle of the path,” he recalled, “But Donato wasn’t scared. He saw the opportunity to contribute to something big and did it very well.”

Going back to Amazon

Crisostomi, who is pursuing his doctorate in computer science at Sapienza, was so enthralled with his first internship experience, he wrote a LinkedIn post about it back in December.

Difference is appreciated at Amazon. If they have chosen you for the position, then they want you to be who you are, not a copy of others.
Donato Crisostomi

“Words will probably fail me to express my gratitude for the time spent here at Amazon, as I didn't imagine I could grow and learn so much in such a brief time,” he wrote. “I would like to dedicate a special thank you from the bottom of my heart to my team in Turin, who made me feel at home while I was far from mine. I am really going to miss you, and hope that our paths will cross again in the future.”

See Amazon's Luxembourg research office
Scientists in Luxembourg solve problems for our global customers and collaborate with teams worldwide. Much of the work in Luxembourg is focused on surfacing the right products to Amazon retail customers and delivering them as efficiently as possible.

That wish, as it turns out, became reality. Crisostomi recently accepted another six-month internship at TEN Search, Amazon’s Luxembourg location.

There, he is focusing on research with an interdisciplinary information retrieval science team engaged in machine learning, natural language processing, transfer learning, ranking, and software development, with the aim of improving the customer experience.

“I feel the same excitement that I felt as a freshman,” Crisostomi remarked. “Providing a pleasant information retrieval experience to the user is as important as it is hard because it requires overcoming language ambiguities. Having the opportunity to assist thrills me.”

That drive to assist extends to offering his advice to prospective interns. “Totally go for it and don’t be afraid of asking questions,” he said. “If you have a chance to visit the office, go and meet people. And this may sound like a cliché, but be yourself. Difference is appreciated at Amazon. If they have chosen you for the position, then they want you to be who you are, not a copy of others.”

It’s a lesson his mother would certainly approve of.

Amazon’s Graduate Research internship program includes mentorship, moderated discussion groups, opportunities to connect with fellow interns, fireside chats with senior leaders, and a variety of networking events. The 2023 Amazon Science internship applications are now open, you can find the latest listings here.

Research areas

Related content

IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will independently file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
IN, KA, Bengaluru
Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. In order to drive expansion of the Amazon catalog, we develop advanced ML/AI technologies to process billions of products and algorithmically find products not already sold on Amazon. We work with structured, semi-structured and Visually Rich Documents using deep learning, NLP and image processing. The role demands a high-performing and flexible candidate who can take responsibility for success of the system and drive solutions from research, prototype, design, coding and deployment. We are looking for Applied Scientists to tackle challenging problems in the areas of Information Extraction, Efficient crawling at internet scale, developing ML models for website comprehension and agents to take multi-step decisions. You should have depth and breadth of knowledge in text mining, information extraction from Visually Rich Documents, semi structured data (HTML) and advanced machine learning. You should also have programming and design skills to manipulate Semi-Structured and unstructured data and systems that work at internet scale. You will encounter many challenges, including: - Scale (build models to handle billions of pages), - Accuracy (requirements for precision and recall) - Speed (generate predictions for millions of new or changed pages with low latency) - Diversity (models need to work across different languages, market places and data sources) You will help us to - Build a scalable system which can algorithmically extract information from world wide web. - Intelligently cluster web pages, segment and classify regions, extract relevant information and structure the data available on semi-structured web. - Build systems that will use existing Knowledge Base to perform open information extraction at scale from visually rich documents. Key job responsibilities - Use AI, NLP and advances in LLMs/SLMs and agentic systems to create scalable solutions for business problems. - Efficiently Crawl web, Automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes. - Design, develop, evaluate and deploy, innovative and highly scalable ML models, esp. leveraging latest advances in RL-based fine tuning methods like DPO, GRPO etc. - Work closely with software engineering teams to drive real-time model implementations. - Establish scalable, efficient, automated processes for large scale model development, model validation and model maintenance. - Lead projects and mentor other scientists, engineers in the use of ML techniques. - Publish innovation in research forums.
US, CA, Santa Clara
We are seeking an Applied Scientist II to join Amazon Customer Service's Science team, where you will build AI-based automated customer service solutions using state-of-the-art techniques in retrieval-augmented generation (RAG), agentic AI, and post-training of large language models. You will work at the intersection of research and production, developing intelligent systems that directly impact millions of customers while collaborating with scientists, engineers, and product managers in a fast-paced, innovative environment. Key job responsibilities - Design, develop, and deploy information retrieval systems and RAG pipelines using embedding models, reranking algorithms, and generative models to improve customer service automation - Conduct post-training of large language models using techniques such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO) to optimize model performance for customer service tasks - Build and curate high-quality datasets for model training and evaluation, ensuring data quality and relevance for customer service applications - Design and implement comprehensive evaluation frameworks, including data curation, metrics development, and methods such as LLM-as-a-judge to assess model performance - Develop AI agents for automated customer service, understanding their advantages and common pitfalls, and implementing solutions that balance automation with customer satisfaction - Independently perform research and development with minimal guidance, staying current with the latest advances in machine learning and AI - Collaborate with cross-functional teams including engineering, product management, and operations to translate research into production systems - Publish findings and contribute to the broader scientific community through papers, patents, and open-source contributions - Monitor and improve deployed models based on real-world performance metrics and customer feedback A day in the life As an Applied Scientist II, you will start your day reviewing metrics from deployed models and identifying opportunities for improvement. You might spend your morning experimenting with new post-training techniques to improve model accuracy, then collaborate with engineers to integrate your latest model into production systems. You will participate in design reviews, share your findings with the team, and mentor junior scientists. You will balance research exploration with practical implementation, always keeping the customer experience at the forefront of your work. You will have the autonomy to drive your own research agenda while contributing to team goals and deliverables. About the team The Amazon Customer Service Science team is dedicated to revolutionizing customer support through advanced AI and machine learning. We are a diverse group of scientists and engineers working on some of the most challenging problems in natural language understanding and AI automation. Our team values innovation, collaboration, and a customer-obsessed mindset. We encourage experimentation, celebrate learning from failures, and are committed to maintaining Amazon's high bar for scientific rigor and operational excellence. You will have access to world-class computing resources, massive datasets, and the opportunity to work alongside some of the brightest minds in AI and machine learning.
US, MA, N.reading
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 revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique 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 dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
US, CA, Sunnyvale
Amazon's AGI Information is seeking an exceptional Applied Scientist to drive science advancements in the Amazon Knowledge Graph team (AKG). AKG is re-inventing knowledge graphs for the LLM era, optimizing for LLM grounding. At the same time, AKG is innovating to utilize LLMs in the knowledge graph construction pipelines to overcome obstacles that traditional technologies could not overcome. As a member of the AKG IR team, you will have the opportunity to work on interesting problems with immediate customer impact. The team is addressing challenges in web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory operating over Graphs. You will also have the opportunity to work with scientists working on the other challenges, and with the engineering teams that deliver the science advancements to our customers. A successful candidate has a strong machine learning and agent background, is a master of state-of-the-art techniques, has a strong publication record, has a desire to push the envelope in one or more of the above areas, and has a track record of delivering to customers. The ideal candidate enjoys operating in dynamic environments, is self-motivated to take on new challenges, and enjoys working with customers, stakeholders, and engineering teams to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems. You will collaborate with applied scientists and engineers to develop novel algorithms and modeling techniques to build the knowledge graph that delivers fresh factual knowledge to our customers, and that automates the knowledge graph construction pipelines to scale to many billions of facts. Your first responsibility will be to solve entity resolution to enable conflating facts from multiple sources into a single graph entity for each real world entity. You will develop generic solutions that work fo all classes of data in AKG (e.g., people, places, movies, etc.), that cope with sparse, noisy data, that scale to hundreds of millions of entities, and that can handle streaming data. You will define a roadmap to make progress incrementally and you will insist on scientific rigor, leading by example.
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 revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As a Senior Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.
JP, 13, Tokyo
Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy. We hire the world's brightest minds and offer them a fast-paced, technologically sophisticated, and collaborative work environment. We are seeking a talented, customer-focused Economist to join our JCI Measurement and Optimization Science Team (JCI MOST). In this role, you will design experiments and build econometric models to measure intervention impacts and deliver data-driven insights that inform leadership decisions. Amazon Economists leverage our world-class data systems to build sophisticated econometric models, drawing from diverse methodological approaches including econometric theory, empirical IO, empirical health, labor, and public economics—all highly valued skillsets at Amazon. You will work in a fast-moving environment solving critical business problems as part of cross-functional teams embedded within business units or our central science and economics organization. This role requires exceptional Causal Inference expertise, strong cross-functional collaboration skills, business acumen, and an entrepreneurial spirit to drive measurable improvements in our pricing quality and business outcomes.
CN, 31, Shanghai
As a Sr. Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques
US, WA, Redmond
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communications Engineer in Modeling and Simulation, this role is primarily responsible for the developing and analyzing high level system resource allocation techniques for links to ensure optimal system and network performance from the capacity, coverage, power consumption, and availability point of view. Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define novel wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance, such as overall throughput, latency, system availability, packet loss, etc., as well as compatibility for both connectivity and interference mitigation with other space and terrestrial systems. This role in particular will be responsible for 1) evaluating complex multi-disciplinary trades involving RF bandwidth and network resource allocation to customers, 2) understanding and designing around hardware/software capabilities and constraints to support a dynamic network topology, 3) developing heuristic or solver-based algorithms to continuously improve and efficiently use available resources, 4) demonstrating their viability through detailed modeling and simulation, 5) working with operational teams to ensure they are implemented. This role will be part of a team developing the necessary simulation tools, with particular emphasis on coverage, capacity, latency and availability, considering the yearly growth of the satellite constellation and terrestrial network. 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 • Work within a project team and take the responsibility for the Leo's overall communication system design and architecture • Extend existing code/tools and create simulation models representative of the target system, primarily in MATLAB • Design interconnection strategies between fronthaul and backhaul nodes. Analyze link availability, investigate link outages, and optimize algorithms to study and maximize network performance • Use RF and optical link budgets with orbital constellation dynamics to model time-varying system capacity • Conduct trade-off analysis to benefit customer experience and optimization of resources (costs, power, spectrum), including optimization of satellite constellation design and link selection • Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements • Analyze and minimize potential self-interference or interference with other communication systems • Provide visualizations, document results, and communicate them across multi-disciplinary project teams to make key architectural decisions
US, WA, Seattle
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 revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced electromechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique 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. Amazon is seeking a talented and motivated Principal Applied Scientist to develop tactile sensors and guide the sensing strategy for our gripper design. The ideal candidate will have extensive experience in sensor development, analysis, testing and integration. This candidate must have the ability to work well both independently and in a multidisciplinary team setting. Key job responsibilities - Author functional requirements, design verification plans and test procedures - Develop design concepts which meet the requirements - Work with engineering team members to implement the concepts in a product design - Support product releases to manufacturing and customer deployments - Work efficiently to support aggressive schedules