Amazon Science celebrates Pi Day

Times Square display honors scientists, engineers, and mathematicians past, present, and future.

  1. Pi Day is an annual celebration of the mathematical constant π (pi), and is observed on March 14 since 3, 1, and 4 are the first three significant digits of π. Pi Day was first observed in 1988, and since then celebrations of the day often have involved eating pie, or holding various mathematical competitions.

    To mark Pi Day this year, Amazon Science utilized a Times Square billboard normally used by Amazon Music to honor scientists, engineers, and mathematicians past, present, and future.

    The billboard display ran from midnight to 8 a.m. and again — for 3 hours and 14 minutes — from 3:14 p.m. to 6:28 p.m. The display (which you can watch above) began by honoring Marie Curie, the first woman to be awarded a Nobel Prize in 1903 for her contributions to physics. It was Curie who once famously said, “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.”

    On Pi Day, we honored some of the scientific giants on whose shoulders we stand, feature a few key accomplishments achieved by Amazon’s researchers and engineers, and highlight some of our university collaborators who are developing the next generation of STEM talent who will help us understand our world more fully, so we may fear less.

    For, as Alan Turing once said, “We can only see a short distance ahead, but we can see plenty there that needs to be done.”

    Below are items highlighted on the Times Square billboard:

  2. Marie Curie — 21911

    Curie — the first and only person to win two Nobel prizes in scientific categories — won her second Nobel Prize in 1911 “in recognition of her services to the advancement of chemistry by the discovery of the elements radium and polonium, by the isolation of radium and the study of the nature and compounds of this remarkable element.”

  3. arXiv — 2035720

    arXiv “hosts more than 2,035,720 scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.” arXiv was founded by Paul Ginsparg in 1991 and is now maintained and operated by Cornell Tech.

  4. Alexa — 11614

    Alexa was introduced to the world on Nov. 6, 2014. Now, eight years later, customers in more than 80 countries interact with Alexa billions of times each week, and Amazon and Lockheed Martin are sending the first Alexa to space as part of Callisto, a technology demonstration on NASA's upcoming Artemis I mission.

  5. Albert Einstein — 1879

    Albert Einstein was born in Ulm, in Württemberg, Germany, on March 14, 1879. He was awarded the Nobel Prize in Physics in 1921 “for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect.”

  6. USC Viterbi School of Engineering — 7,930,681,085

    USC’s engineering school is named for Andrew Viterbi who earned one of the first doctorates in electrical engineering ever granted at USC. The Viterbi Algorithm, a mathematical formula to eliminate signal interference, paved the way for the widespread use of cellular technology.

  7. Amazon — Lab126

    Located in Sunnyvale, Calif., the team at Lab126 designs and engineers the Amazon Astro robot, Fire tablets, Kindle e-readers, Amazon Fire TV, Amazon Echo, and other devices. They’re also exploring how devices are accidentally damaged — and how to help ensure they survive more of those incidents.

  8. Katherine Johnson — 71962

    Katherine Johnson did the trajectory analysis for NASA’s Friendship 7 before it launched in 1962. The complexity of the flight required the construction of a worldwide communications network, but astronauts were wary of the electronic calculating machines. John Glenn asked to have Johnson run the numbers by hand. “If she says they’re good,’” Johnson recalled Glenn saying, “then I’m ready to go.” Glenn’s flight was a success.

  9. Columbia Engineering — 1150

    Edwin Armstrong developed FM radio on Columbia's campus at 1150 Amsterdam Avenue in New York. During an IRE conference in 1935, he described FM radio “and then turned on his receiver in front of the audience. An FM transmission … came in totally free of static and, thanks to the wide audio spectrum being used, with a fidelity never heard before. A stunned audience listened to a live music performance transmitted with remarkable clarity and to a series of sounds, such as a glass of water being poured or a piece of paper being torn — which would have been unrecognizable over AM radio.”

  10. Amazon S3 — 16

    Amazon S3 (Simple Storage Service) is an object storage service that offers industry-leading scalability, data availability, security, and performance. It was launched 16 years ago on Pi Day, as the first generally available AWS service. At the 2021 ACM Symposium on Operating Systems Principles, Amazon researchers won a best-paper award for their work using automated reasoning to validate that ShardStore — a new S3 storage node microservice — will do what it’s supposed to.

  11. Alan Turing — 186

    Alan Turing, WWII enigma codebreaker and father of computer science, had an IQ of 186. In 1935, when Turing was 22 years old, he wrote a paper titled, “On Computable Numbers, With an Application to the Entscheidungsproblem” (German for “decision problem”). The paper is widely recognized as having provided “a conceptual model for modern computers.”

  12. UCLA Samueli School of Engineering — 3420

    The first internet transmission was sent on Oct. 29, 1969 from 3420 Boelter Hall at UCLA. The message sent to Stanford Research Institute on the evening of Oct. 29 was meant to be “LOGIN.” However, the system crashed, so the first message sent over the internet was: “LO.” The next attempt was a success, and the original Interface Message Processor, known as IMP No. 1, the equivalent to today’s router, still stands in Boelter Hall.

  13. Amazon Research Awards — 385132

    Amazon, via the Amazon Research Awards (ARA) program, has funded more than 385 research proposals at 132 universities since 2018. The ARA program funds academic research and related contributions to open-source projects by top academic researchers who have used their research awards to help improve MRI scans, create computational simulations of the human heart, understand the impact of aerosol-cloud interactions on climate, and teach robots to behave like schools of fish.

  14. Grace Hopper — 6

    The Cray XE6 “Hopper” supercomputer was named after computer science pioneer Grace Hopper. One of the first three computer “programmers,” Hopper was responsible for programming the Mark I. As head programmer for Remington Rand, she worked on the UNIVAC I (Universal Automatic Computer). In 1952 her programming team developed the first computer language “compiler” called A-0. Her team also developed Flow-Matic, the first programming language to use English-like commands.

  15. Caltech — 9019

    Scientists and engineers at Caltech and NASA’s Jet Propulsion Laboratory (JPL) landed the Mars Pathfinder on Mars on July 4, 1997 and have been operating rovers ever since — or for 9019 Earth days on March 14. Over a three-month period, Pathfinder returned 2.3 billion bits of information, including more than 16,500 images from the lander and 550 images from the rover, as well as more than 15 chemical analyses of rocks and soil and extensive data on winds and other weather factors.

  16. Blue Origin — 19314

    New Shepard has flown 19 consecutive successful launches, including 3 flights with a total of 14 astronauts aboard. Named after Mercury astronaut Alan Shepard, the first American to go to space, New Shepard is “a reusable suborbital rocket system designed to take astronauts and research payloads past the Kármán line – the internationally recognized boundary of space.”

  17. Georgia Tech — 161972

    John Young, who became the 9th person to walk on the moon in 1972, graduated from Georgia Tech's Guggenheim School of Aerospace Engineering. Young served as commander of Apollo 16, spending three nights on the moon’s surface and driving 16 miles in a lunar rover. Young was also at the helm for the first space shuttle mission, STS-1, in April 1981.

  18. University of Washington — 14492

    Waldo Semon, a University of Washington alumnus, conducted 14,492 experiments to develop synthetic rubber used during WWII. Semon is best known for inventing vinyl, the world's second most-used plastic. Semon held 116 patents, and was inducted into the Invention Hall of Fame in 1995 at age 97.

  19. Alexa Prize — 24806391

    There have been more than 24,806,391 interactions with Alexa Prize socialbots. That number will only continue to climb as the competition meant to help advance the science of conversational AI continues. Last year, Team Alquist from Czech Technical University won the Alexa Prize SocialBot Grand Challenge 4 competition.

Related content

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.
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 open research problems at the intersection of GenAI, multimodal reasoning, and large-scale information retrieval—defining the scientific questions that transform ambiguous, real-world catalog challenges into publishable, high-impact research * Push the boundaries of VLMs, foundation models, and agentic architectures by designing novel approaches to product identity, relationship inference, and catalog understanding—where the problem complexity (billions of products, multimodal signals, inherent ambiguity) demands methods that don't yet exist * Advance the science of efficient model deployment—developing distillation, compression, and LLM/VLM serving optimization strategies that preserve frontier-level multimodal reasoning in compact, production-grade architectures while dramatically reducing latency, cost, and infrastructure footprint at billion-product scale * Make frontier models reliable—advancing uncertainty calibration, confidence estimation, and interpretability methods so that frontier-scale GenAI systems can be trusted for autonomous catalog decisions impacting millions of customers daily * Own the full research lifecycle from problem formulation through production deployment—designing rigorous experiments over petabytes of multimodal data, iterating on ideas rapidly, and seeing your research directly improve the shopping experience for hundreds of millions of customers * Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact * Mentor scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building deep 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