Quantum key distribution and authentication: Separating facts from myths

Key exchange protocols and authentication mechanisms solve distinct problems and must be integrated in a secure communication system.

Quantum key distribution (QKD) is a technology that leverages the laws of quantum physics to securely share secret information between distant communicating parties. With QKD, quantum-mechanical properties ensure that if anyone tries to tamper with the secret-sharing process, the communicating parties will know. Keys established through QKD can then be used in traditional symmetric encryption or with other cryptographic technologies to secure communications.

“Record now, decrypt later" (RNDL) is a cybersecurity risk arising from advances in quantum computing. The term refers to the situation in which attackers record encrypted data today, even though they cannot decrypt it immediately. They store this data with the expectation that future quantum computers will be powerful enough to break the cryptographic algorithms currently securing it. Sensitive information such as financial records, healthcare data, or state secrets could be at risk, even years after it was transmitted.

Mitigating RNDL requires adopting quantum-resistant cryptographic methods, such as post-quantum cryptography (PQC) and/or quantum key distribution (QKD), to ensure confidentiality against future quantum advancements. AWS has invested in the migration to post-quantum cryptography to protect the confidentiality, integrity, and authenticity of customer data.

Quantum communication is important enough that in 2022, three of its pioneers won the Nobel Prize for physics. However, misconceptions about QKD’s role still persist. One of them is that QKD lacks practical value because it “doesn’t solve the authentication problem”. This view can obscure the broad benefits that QKD brings to secure communications when integrated properly into existing systems.

QKD should be viewed as a complement to — rather than a replacement for — existing cybersecurity frameworks. Functionally, QKD solves the same problem solved by other key establishment protocols, including the well-known Diffie-Hellman (DH) method and the module-lattice-based key encapsulation mechanism (ML-KEM), the standard recently ratified by the FIPS — but it does it in a fundamentally different way. Like those methods, QKD depends on strong authentication to defend against threats such as man-in-the-middle attacks, where an attacker poses as one of the communicating parties.

Related content
The head of Amazon Web Services’ quantum communication program on the Nobel winners’ influence on her field.

In short, key exchange protocols and authentication mechanisms are different security primitives for solving distinct problems and must be integrated together in a secure communication system.

The challenge, then, is not to give QKD an authentication mechanism but to understand how it can be integrated with other established mechanisms to strengthen the overall security infrastructure. As quantum technologies continue to evolve, it’s important to shift the conversation from skepticism about authentication to consideration of how QKD can be thoughtfully and practically implemented to address today’s and tomorrow’s cybersecurity needs — such as the need to mitigating the “record now, decrypt later” (RNDL) attack (see sidebar).

Understanding the role of authentication in QKD

When discussing authentication in the context of QKD, we focus on the classical digital channel that the parties use to exchange information about their activities on the quantum channel. This isn’t about user authentication methods, such as logging in with passwords or biometrics, but rather about authenticating the communicating entities and the data exchanged. Entity authentication ensures that the parties are who they claim to be; data authentication guarantees that the information received is the same as what was sent by the claimed source. QKD protocols include a classical-communication component that uses both authentication methods to assure the overall security of the interaction.

Entity authentication

Entity authentication is the process by which one party (the "prover") asserts its identity, and another party (the "verifier") validates that assertion. This typically involves a registration step, in which the verifier obtains reliable identification information about the prover, as a prelude to any further authentication activity. The purpose of this step is to establish a “root of trust” or “trust anchor”, ensuring that the verifier has a trusted baseline for future authentications.

Related content
Collaboration will seek to advance the development of a quantum internet.

Several entity authentication methods are in common use, each based on a different type of trust anchor:

  • Public-key-infrastructure (PKI) authentication: In this method, a prover’s certificate is issued by a trusted certificate authority (CA). The verifier relies on this CA, or the root CA in a certificate chain, to establish trust. The certificate acts as the trust anchor that links the prover’s identity to its public key.
  • PGP-/GPG-based (web of trust) authentication: Here, trust is decentralized. A prover’s public key is trusted if it has been vouched for by one or more trusted third parties, such as a mutual acquaintance or a public-key directory. These third parties serve as the trust anchors.
  • Pre-shared-key-based (PSK) authentication: In this case, both the prover and the verifier share a secret key that was exchanged via an offline or other secure out-of-band method. The trust anchor is the method of securely sharing this key a priori, such as a secure courier or another trusted channel.

These trust anchors form the technical backbones of all authentication systems. However, all entity authentication methods are based on a fundamental assumption: the prover is either the only party that holds the critical secret data (e.g., the prover’s private key in PKI or PGP) or the only other party that shares the secret with the verifier (PSK). If this assumption is broken — e.g., the prover's private key is stolen or compromised, or the PSK is leaked — the entire authentication process can fail.

Data authentication

Data authentication, also known as message authentication, ensures both the integrity and authenticity of the transmitted data. This means the data received by the verifier is exactly what the sender sent, and it came from a trusted source. As with entity authentication, the foundation of data authentication is the secure management of secret information shared by the communicating parties.

Related content
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.

The most common approach to data authentication is symmetric cryptography, where both parties share a secret key. A keyed message authentication code (MAC), such as HMAC or GMAC, is used to compute a unique tag for the transmitted data. This tag allows the receiver to verify that the data hasn’t been altered during transit. The security of this method depends on the collision resistance of the chosen MAC algorithm — that is, the computational infeasibility of finding two or more plaintexts that could yield the same tag — and the confidentiality of the shared key. The authentication tag ensures data integrity, while the secret key guarantees the authenticity of the data origin.

An alternative method uses asymmetric cryptography with digital signatures. In this approach, the sender generates a signature using a private key and the data itself. The receiver, or anyone else, can verify the signature’s authenticity using the sender’s public key. This method provides data integrity through the signature algorithm, and it assures data origin authenticity as long as only the sender holds the private key. In this case, the public key serves as a verifiable link to the sender, ensuring that the signature is valid.

In both the symmetric and the asymmetric approaches, successful data authentication depends on effective entity authentication. Without knowing and trusting the identity of the sender, the verification of the data’s authenticity is compromised. Therefore, the strength of data authentication is closely tied to the integrity of the underlying entity authentication process.

Authentication in QKD

The first quantum cryptography protocol, known as BB84, was developed by Bennett and Brassard in 1984. It remains foundational to many modern QKD technologies, although notable advancements have been made since then.

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

QKD protocols are unique because they rely on the fundamental principles of quantum physics, which allow for “information-theoretic security.” This is distinct from the security provided by computational complexity. In the quantum model, any attempt to eavesdrop on the key exchange is detectable, providing a layer of security that classical cryptography cannot offer.

QKD relies on an authenticated classical communication channel to ensure the integrity of the data exchanged between parties, but it does not depend on the confidentiality of that classical channel. (This is why RNDL is not an effective attack against QKD). Authentication just guarantees that the entities establishing keys are legitimate, protecting against man-in-the-middle attacks.

Currently, several commercial QKD products are available, many of which implement the original BB84 protocol and its variants. These solutions offer secure key distribution in real-world applications, and they all pair with strong authentication processes to ensure the communication remains secure from start to finish. By integrating both technologies, organizations can build communication infrastructures capable of withstanding both classical and quantum threats.

Authentication in QKD bootstrap: A manageable issue

During the initial bootstrap phase of a QKD system, the authentic classical channel is established using traditional authentication methods based on PKI or PSK. As discussed earlier, all of these methods ultimately rely on the establishment of a trust anchor.

Related content
Automated reasoning and optimizations specific to CPU microarchitectures improve both performance and assurance of correct implementation.

While confidentiality may need to be maintained for an extended period (sometimes decades), authentication is a real-time process. It verifies identity claims and checks data integrity in the moment. Compromising an authentication mechanism at some future point will not affect past verifications. Once an authentication process is successfully completed, the opportunity for an adversary to tamper with it has passed. That is, even if, in the future, a specific authentication mechanism used in QKD is broken by a new technology, QKD keys generated prior to that point are still safe to use, because no adversary can go back in time to compromise past QKD key generation.

This means that the reliance on traditional, non-QKD authentication methods presents an attack opportunity only during the bootstrap phase, which typically lasts just a few minutes. Given that this phase is so short compared to the overall life cycle of a QKD deployment, the potential risks posed by using authentication mechanisms are relatively minor.

Authentication after QKD bootstrap: Not a new issue

Once the bootstrap phase is complete, the QKD devices will have securely established shared keys. These keys can then be used for PSK-based authentication in future communications. In essence, QKD systems can maintain the authenticated classical communication channel by utilizing a small portion of the very keys they generate, ensuring continued secure communication beyond the initial setup phase.

It is important to note that if one of the QKD devices is compromised locally for whatever reason, the entire system’s security could be at risk. However, this is not a unique vulnerability introduced by QKD. Any cryptographic system faces similar challenges when the integrity of an endpoint is compromised. In this respect, QKD is no more susceptible to it than any other cryptographic system.

Overcoming key challenges to QKD’s role in cybersecurity

Up to now we have focused on clarifying the myths about authentication needs in QKD. Next we will discuss several other challenges in using QKD in practice.

Bridging the gap between QKD theory and implementation

While QKD protocols are theoretically secure, there remains a significant gap between theory and real-world implementations. Unlike traditional cryptographic methods, which rely on well-understood algorithms that can be thoroughly reviewed and certified, QKD systems depend on specialized hardware. This introduces complexity, as the process of reviewing and certifying QKD hardware is not yet mature.

Related content
Using time to last byte — rather than time to first byte — to assess the effects of data-heavy TLS 1.3 on real-world connections yields more encouraging results.

In conventional cryptography, risks like side-channel attacks — which use runtime clues such as memory access patterns or data retrieval times to deduce secrets — are well understood and mitigated through certification processes. QKD systems are following a similar path. The European Telecommunications Standards Institute (ETSI) has made a significant move by introducing the Common Criteria Protection Profile for QKD, the first international effort to create a standardized certification framework for these systems. ISO/IEC has also published standards on security requirements and test and evaluation methods for QKD. These represent crucial steps in building the same level of trust that traditional cryptography enjoys.

Once the certification process is fully established, confidence in QKD’s hardware implementations will continue to grow, enabling the cybersecurity community to embrace QKD as a reliable, cutting-edge solution for secure communication. Until then, the focus remains on advancing the review and certification processes to ensure that these systems meet the highest security standards.

QKD deployment considerations

One of the key challenges in the practical deployment of QKD is securely transporting the keys generated by QKD devices to their intended users. While it’s accepted that QKD is a robust mechanism for distributing keys to the QKD devices themselves, it does not cover the secure delivery of keys from the QKD device to the end user (or key consumer).

QKD diagram.png
A schematic representation of two endpoints — site A and site B — that want to communicate safely. The top line represents the user traffic being protected, and the bottom lines are the channels required to establish secure communication. An important practical consideration is how to transmit a key between a QKD device and an end user within an endpoint.

This issue arises whether the QKD system is deployed within a large intranet or a small local-area network. In both cases, the keys must be transported over a non-QKD system. The standard deployment requirement is that the key delivery from the QKD system to the key consumer occurs “within the same secure site”, and the definition of a “secure site” is up to the system operator.

Related content
Prize honors Amazon senior principal scientist and Penn professor for a protocol that achieves a theoretical limit on information-theoretic secure multiparty computation.

The best practice is to make the boundary of the secure site as small as is practical. One extreme option is to remove the need for transporting keys over classical networks entirely, by putting the QKD device and the key user’s computing hardware in the same physical unit. This eliminates the need for traditional network protocols for key transport and realizes the full security benefits of QKD without external dependency. In cases where the extreme option is infeasible or impractical, the secure site should cover only the local QKD system and the intended key consumers.

Conclusion

QKD-generated keys will remain secure even when quantum computers emerge, and communications using these keys are not vulnerable to RNDL attacks. For QKD to reach its full potential, however, the community must collaborate closely with the broader cybersecurity ecosystem, particularly in areas like cryptography and governance, risk, and compliance (GRC). By integrating the insights and frameworks established in these fields, QKD can overcome its current challenges in trust and implementation.

This collective effort is essential to ensure that QKD becomes a reliable and integral part of secure communication systems. As these collaborations deepen, QKD will be well-positioned to enhance existing security frameworks, paving the way for its adoption across industries and applications.

Related content

US, WA, Seattle
Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and exclusive access to coverage of live sports. All customers regardless of whether they have a Prime membership or not, can access programming from subscriptions such as Apple TV, Peacock Premium Plus, HBO Max, FOX One, Crunchyroll and MGM+, as well as more than 900 free ad-support (FAST) Channels, rent or buy titles, and enjoy even more content for free with ads. The Prime Video Personalization and Discovery team matches customers with the right content at the right time, at all touch points throughout the content discovery journey. We are looking for a customer-focused, solutions-oriented Data Scientist to help build new data-driven frameworks to understand what makes new personalization and content discovery innovations successful for users and the business. You'll be part of an embedded science team on projects that are fast-paced, challenging, and ultimately influence what millions of customers around the world see when the log into Prime Video. The ideal candidate brings strong problem-solving skills, stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will build cross-functional support within Prime Video, assess business problems, define metrics, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities - Use advanced statistical and machine learning techniques to extract insights from complex, large-scale data sets - Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment - Support scalable, self-service data analyses by building datasets for analytics, reporting and ML use cases - Partner with product stakeholders and senior science peers to identify strategic data-driven opportunities to improve the customer experience - Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders - Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.
US, CA, San Diego
Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.
IN, KA, Bengaluru
The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. As an Applied Science Manager on the team, you will lead a team of scientists to define and execute a transformative vision for holistic catalog quality measurement, metadata enrichment, and content integrity. Your team will own the science solutions for foundational quality detection frameworks, metadata validation and correction technologies, state-of-the-art algorithms to identify and resolve catalog anomalies (violative content, duplicative/low value content, misattributed tracks, incorrect metadata), and/or agentic AI solutions that help internal teams quickly surface and fix quality issues to ensure customers receive accurate, complete catalog experiences. Key job responsibilities You independently manage a team of scientists. You identify the needs of your team and effectively grow, hire, and promote scientists to maintain a high-performing team. You have a broad understanding of scientific techniques, several of which may fall out of your specific job function. You define the strategic vision for your team. You establish a roadmap and successfully deliver scientific solutions that innovate on catalog quality detection, metadata enrichment, and content integrity. You define clear goals for your team and effectively prioritize, balancing short-term quality improvements and long-term innovation in catalog intelligence. You establish clear and effective metrics and scientific process to enforce consistent, high-quality artifact delivery and measurable catalog quality improvements. You proactively identify risks and bring them to the attention of your manager, customers, and stakeholders with plans for mitigation before they become roadblocks. You know when to escalate. You communicate ideas effectively, both verbally and in writing, to all types of audiences. You author strategic documentation for your team. You communicate issues and options with leaders in such a way that facilitates understanding and that leads to a decision. You work successfully with customers, leaders, and engineering teams. You foster a constructive dialogue, harmonize discordant views, and lead the resolution of contentious issues. About the team We are a team of scientists and MLEs focused on music catalog quality and metadata intelligence. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and data quality systems with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, content operations teams, and other scientists with expertise in music metadata, content classification, and building scalable modeling and software solutions that keep the Amazon Music catalog accurate, complete, and trustworthy.
US, CA, San Diego
Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.
US, WA, Seattle
Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.
BR, SP, Sao Paulo
Esta é uma posição de colaborador individual, com base em nosso escritório de São Paulo. Procuramos uma pessoa dinâmica, analítica, inovadora, orientada para a prática e com foco inabalável no cliente. Na Amazon, nosso objetivo é exceder as expectativas dos clientes, garantindo que seus pedidos sejam entregues com máxima rapidez, precisão e eficiência de custo. A determinação da rota de cada pacote é realizada por sistemas complexos, que precisam acompanhar o crescimento acelerado e a complexidade da malha logística no Brasil. Diante desse cenário, a equipe de Otimização de Supply Chain está à procura de um cientista de dados experiente, capaz de desenvolver modelos, ferramentas e processos para garantir confiabilidade, agilidade, eficiência de custos e a melhor utilização dos ativos. O candidato ideal terá sólidas habilidades quantitativas e experiência com conjuntos de dados complexos, sendo capaz de identificar tendências, inovar processos e tomar decisões baseadas em dados, considerando a cadeia de suprimentos de ponta a ponta. Key job responsibilities * Executar projetos de melhoria contínua na malha logística, aproveitando boas práticas de outros países e/ou desenvolvendo novos modelos. * Desenvolver modelos de otimização e cenários para planejamentos logísticos. * Criar modelos de otimização voltados para a execução de eventos e períodos de alta demanda. Automatizar processos manuais para melhorar a produtividade da equipe. * Auditar operações, configurações sistêmicas e processos que possam impactar custos, produtividade e velocidade de entregas. * Realizar benchmarks com outros países para identificar melhores práticas e processos avançados, conectando-os às operações no Brasil. About the team Nosso time é composto por engenheiros de dados, gerentes de projetos e cientistas de dados, todos dedicados a criar soluções escaláveis e inovadoras que suportem e otimizem as operações logísticas da Amazon no Brasil. Nossa missão é garantir a eficiência de todas as etapas da cadeia de suprimentos, desde a primeira até a última milha, ajudando a Amazon a entregar resultados com agilidade, precisão e a um custo competitivo, especialmente em um ambiente de rápido crescimento e complexidade.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Member of Technical Staff with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Member of Technical Staff with the AGI team, you will support the development of algorithms and modeling techniques, to advance the state of the art with LLMs. You will support the foundational model development in an applied research role, including model training, dataset design, and pre- and post-training optimization. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.