Antia Lamas-Linares, quantum networking lead at Amazon Web Services and an expert in quantum optics, is seen looking into the camera
Antia Lamas-Linares, quantum networking lead at Amazon Web Services and an expert in quantum optics, was among the ‘first wave’ of scientists to gain a PhD in quantum technology.

Antia Lamas-Linares’s path into the world of quantum

Among the ‘first wave’ of scientists to gain a PhD in quantum technology, the senior manager of research science discusses her two-decade-long career journey.

In January 2021, Antia Lamas-Linares joined Amazon Web Services (AWS) to work on quantum technologies.

A quantum information scientist, Lamas-Linares is an expert in quantum optics. More precisely, in photonic (optical) implementations of quantum-information protocols. Her career to date includes pioneering research on quantum key distribution — formerly known as quantum cryptography — superconducting single-photon detectors and space-based quantum technology (including several patents), in addition to high-performance computing.

Related content
Researchers affiliated with Amazon Web Services' Center for Quantum Computing are presenting their work this week at the Conference on Quantum Information Processing.

Quantum science and technologies are evolving fast, and for the first time, small prototype quantum computers are appearing around the world. Indeed, the Amazon Braket service provides access to these computers for researchers and institutions. AWS itself announced the opening of its Center for Quantum Computing in October 2021. While quantum processors already exhibit some interesting quantum mechanical behaviors, they have some way to go before they outperform “classical” computers in truly disruptive ways.

Quantum computers work through the manipulation of quantum bits, known as qubits, instead of conventional digital bits. Lamas-Linares joined AWS to focus on research related to connecting quantum devices with each other.

“You can think of quantum computing as dealing with stationary qubits and quantum networking as dealing with ‘flying qubits’ – qubits going from A to B,” says Lamas-Linares.

Quantum networking

To understand the coming importance of quantum networking, first consider a central disruption that quantum computers are ultimately expected to deliver: a potential, future threat to modern digital security. That is because quantum computers have the potential to outperform classical computers, including the ability to break encryption methods currently relied on for modern communications and data security.

The center's mission is to address fundamental scientific and engineering challenges and to develop new hardware, software, and applications for quantum networks.

“This once-remote threat of a hypothetical quantum computer breaking modern encryption is becoming less of a hypothetical and more of a ‘not if but when’,” says Lamas-Linares.

One potential solution to this challenge would be go “full quantum” in how information is protected in the first place, says Lamas-Linares, using quantum encryption keys.

“One of the main applications — and low-hanging fruit — of quantum networking is the ability to distribute those keys securely. This involves exploiting the inherent randomness and correlations that exist in quantum systems to create perfectly secure correlated numbers that can then be used for cryptography.”

In short, quantum networking has the potential to also deliver perfect privacy. It would be easy to fall down a quantum rabbit hole here. Suffice it to say, quantum entanglement — a fundamental quantum phenomenon — can be exploited to distribute these keys in such a way that no intermediary company involved in the warehousing or transmitting of data would be able to access that data.

The challenge and promise of quantum computing | Amazon Science

Only the possessor of the quantum keys — the data owners — can decrypt and access that data. In the future, such perfect protection of customer data will be crucial to every organization, from financial institutions and governments to hospitals and industry. The goal of quantum key distribution is to securely transmit those keys to where they need to be.

“At AWS we often say that security is job zero — more important than any other priority. That’s because if customers don't trust the cloud, then most business models just won’t work in the cloud. Customers need confidence that their data and transactions are secure,” says Lamas-Linares.

Mathematical games

The first quantum cryptography protocol, theoretical but provably secure, was called BB84 and published in 1984. At the time, a young Lamas-Linares was growing up in Santiago, Spain, busily getting hooked on mathematics and physics: she did not yet speak English, but recalls her parents owned the Spanish translation of a collection of classic “Mathematical Games” columns from Scientific American, written by Martin Gardner.

“That really caught my attention — I was fascinated,” says Lamas-Linares. Later, in 1988, Stephen Hawking’s “The Brief History of Time” further captivated her. “It’s kind of a cliché, but that book set me on my path.”

Related content
New method enables entanglement between vacancy centers tuned to different wavelengths of light.

It was a path that took Lamas-Linares to study physics at the University of Santiago de Compostela. After graduating, Lamas-Linares moved for a year to the University of Sheffield, UK, via the European Union’s Erasmus student exchange program, before spending a year completing a master’s in applied optics at Imperial College London.

Why the focus on optics?

[Optics] is one of these fields in physics where you can literally see the things that are happening. If you study optics from a mathematical point of view, it’ll tell you something that you can recreate perfectly with light and lenses.
Antia Lamas-Linares

“It’s one of these fields in physics where you can literally see the things that are happening. If you study optics from a mathematical point of view, it’ll tell you something that you can recreate perfectly with light and lenses. I thought that was really cool,” she said.

Then Lamas-Linares started learning about quantum optics, and so-called “squeezed states” of light. Being quantum, and therefore tiny, this is physics you cannot see with your eyes, but she thought it was cooler still. In 2003, Lamas-Linares completed her doctorate in physics at the University of Oxford.

Lamas-Linares’s subsequent career has continued an international trend. Highlights include becoming an assistant professor at the National University of Singapore (NUS), where she soon set up a new quantum optics lab and became principal investigator at the university’s Centre for Quantum Technologies. She later became a senior research fellow at the US National Institute of Standards and Technology in Boulder, Colorado, and a research associate doing high performance computing at the Texas Advanced Computing Center in Austin.

Moving into industry

When Lamas-Linares made the move from academia to industry, it was to join an NUS spinout company, SpeQtral, as chief quantum scientist in 2019. The switch resulted from an itch for her work to have more direct real-world impact.

“Academia is full of what we call hero experiments, where you make something work once, but maybe afterward it self-destructs or melts or something; the important thing is you showed something was possible; a viable effect. That’s great, but it’s nowhere near what you need to create a useful technology,” says Lamas-Linares. “First and foremost, I'm an experimentalist — I build devices. And I wanted to build robust versions of useful technology. That sort of engineering challenge doesn’t make sense for academia — you have to go to industry. I want to bring quantum technologies to the point where it is the ‘best’ solution to a technical problem and so it becomes part of the standard toolbox.”

Women in Quantum Summit - Antia Lamas Linares

SpeQtral pioneers the development of miniaturized sources of quantum-entangled photons, designed to be deployed on satellites as a means to distribute quantum keys around the Earth. The company has successfully demonstrated such miniaturized technology in space, using its shoebox sized “cubesat”, SpooQy-1.

“SpeQtral had already put an entanglement source in space when I joined as chief quantum scientist,” Lamas-Linares recalls. “By this time I’d been working in the field for two decades, having done a lot of work on entanglement sources, but also on whole systems designed to implement quantum key distribution systems over free space, and in hacking those same systems to show which parts needed further thought.”

While at SpeQtral, industry networking meant Lamas-Linares talked with Amazon about this technology. “That’s how I became more aware of what that Amazon was doing things in quantum technologies,” says Lamas-Linares. “It turned out that one of my former colleagues, Simone Severini, was working at AWS in quantum computing. One day he said to me: ‘Hey, we're doing really interesting stuff. Would you be interested in joining us?’.”

What was it that Severini saw in Lamas-Linares?

“I’ve known Antia professionally for about 20 years, and have always been struck by her adaptability and the fact that she is a real ‘owner’,” he says. “Ownership is fundamental in a complex, pioneering environment like this. Nobody is telling you exactly what to do — you have to find your own way, and push when you find friction. “Antia fits Amazon very well — she has a strong bias for action.”

Amazon’s appeal

For Lamas-Linares’s part, she was attracted to Amazon’s resources, capability, and very long-term vision.

“Amazon is only interested in building things that have a clear application and benefit for their customers, but if they are convinced of that customer value, they will invest for as many years as necessary to reach the required level of technological readiness,” she explained. “That’s exciting, and it’s much harder to do in the start-up/venture capitalist environment, particularly with complicated hardware products.”

Related content
New phase estimation technique reduces qubit count, while learning framework enables characterization of noisy quantum systems.

One of the main challenges in making strides in quantum networking, says Lamas-Linares, is technological integration.

“Whatever quantum technology you develop, before it can be of any use to your customers, an entire ecosystem of additional technology needs to be built up around it, and the people needed to do that barely exist for quantum technologies. Finding that combination of expertise and building the required tools is a non-trivial challenge.”

As quantum technologies are taken up by industry, we’re starting to make the molds for what quantum engineers will be. That, to me, is really exciting.
Antia Lamas-Linares

The sheer newness of many quantum technologies makes it tricky to orchestrate a successful career in the field. Does Lamas-Linares, herself in the first wave of scientists to gain a PhD in quantum technology, have any advice to offer?

“I am definitively not qualified to give anyone advice, but I would say this: Don’t be afraid to take an unconventional path. Especially in emerging fields like this, you just don’t know what the right combination of skills and experience will turn out to be.”

Lamas-Linares points out that “quantum engineers” don’t really exist as yet.

“Engineers take established knowledge and they perfect it. As quantum technologies are taken up by industry, we’re starting to make the molds for what quantum engineers will be. That, to me, is really exciting.”

Related content

US, CA, San Francisco
Amazon launched the AGI Lab to develop foundational capabilities for useful AI agents. We built Nova Act - a new AI model trained to perform actions within a web browser. The team builds AI/ML infrastructure that powers our production systems to run performantly at high scale. We’re also enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities This role will lead a team of SDEs building AI agents infrastructure from launch to scale. The role requires the ability to span across ML/AI system architecture and infrastructure. You will work closely with application developers and scientists to have a impact on the Agentic AI industry. We're looking for a Software Development Manager who is energized by building high performance systems, making an impact and thrives in fast-paced, collaborative environments. About the team Check out the Nova Act tools our team built on on nova.amazon.com/act
US, CA, Santa Clara
Amazon Quick Suite is an enterprise AI platform that transforms how organizations work with their data and knowledge. Combining generative AI-powered search, deep research capabilities, intelligent agents and automations, and comprehensive business intelligence, Quick Suite serves tens of thousands of users. Our platform processes thousands of queries monthly, helping teams make faster, data-driven decisions while maintaining enterprise-grade security and governance. From natural language interactions with complex datasets to automated workflows and custom AI agents, Quick Suite is redefining workplace productivity at unprecedented scale. We are seeking a Data Scientist II to join our Quick Data team, focusing on evaluation and benchmarking data development for Quick Suite features, with particular emphasis on Research and other generative AI capabilities. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Quick Suite. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users at scale. As part of our diverse team—including data scientists, engineers, language engineers, linguists, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. Key job responsibilities In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Specific responsibilities include: * Design and develop comprehensive evaluation and benchmarking datasets for Quick Suite AI-powered features * Leverage LLMs for synthetic data corpora generation; data evaluation and quality assessment using LLM-as-a-judge settings * Create ground truth datasets with high-quality question-answer pairs across diverse domains and use cases * Lead human annotation initiatives and model evaluation audits to ensure data quality and relevance * Develop and refine annotation guidelines and quality frameworks for evaluation tasks * Conduct statistical analysis to measure model performance, identify failure patterns, and guide improvement strategies * Collaborate with ML scientists and engineers to translate evaluation insights into actionable product improvements * Build scalable data pipelines and tools to support continuous evaluation and benchmarking efforts * Contribute to Responsible AI initiatives by developing safety and fairness evaluation datasets About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
IN, KA, Bengaluru
Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.
IN, KA, Bengaluru
Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.
IN, KA, Bengaluru
Amazon Health Services (One Medical) About Us: At Health AI, we're revolutionizing healthcare delivery through innovative AI-enabled solutions. As part of Amazon Health Services and One Medical, we're on a mission to make quality healthcare more accessible while improving patient outcomes. Our work directly impacts millions of lives by empowering patients and enabling healthcare providers to deliver more meaningful care. Role Overview: We're seeking an Applied Scientist to join our dynamic team in building state of the art AI/ML solutions for healthcare. This role offers a unique opportunity to work at the intersection of artificial intelligence and healthcare, developing solutions that will shape the future of medical services delivery. Key job responsibilities • Lead end-to-end development of AI/ML solutions for Amazon Health organization, including Amazon Pharmacy and One Medical • Research, design, and implement state-of-the-art machine learning models, with a focus on Large Language Models (LLMs) and Visual Language Models (VLMs) • Optimize and fine-tune models for production deployment, including model distillation for improved latency • Drive scientific innovation while maintaining a strong focus on practical business outcomes • Collaborate with cross-functional teams to translate complex technical solutions into tangible customer benefits • Contribute to the broader Amazon Health scientific community and help shape our technical roadmap
US, CA, Pasadena
The Amazon Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist specializing in Mixed-Signal Design. Working alongside other scientists and engineers, you will design and validate hardware performing the control and readout functions for AWS quantum processors. Candidates must have a solid background in mixed-signal design at the printed circuit board (PCB) level. Working effectively within a cross-functional team environment is critical. The ideal candidate will have demonstrated the capability to contribute to all phases of product life cycle development, from requirements gathering to verification. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Key job responsibilities Our scientists and engineers collaborate across diverse teams and projects to offer state of the art, cost effective solutions for the control of Amazon quantum processor systems. You’ll bring a passion for innovation, collaboration, and mentoring to: Solve layered technical problems, often ones not encountered before, across our hardware stack. Develop requirements with key system stakeholders, including quantum device, test and measurement, and cryogenic hardware teams. Design, implement, test, deploy, and maintain innovative solutions that meet both strict performance and cost metrics. Research enabling control system technologies necessary for Amazon to produce commercially viable quantum computers.
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.