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, NY, New York
We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans. Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions. - Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation
US, CA, Sunnyvale
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 programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video, audio, and text. Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories. Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences. Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses. Effectively communicate technical and non-technical ideas with teammates and stakeholders. Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis. Publish your research findings in top conferences and journals. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, CA, Sunnyvale
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 programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video and text Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses Effectively communicate technical and non-technical ideas with teammates and stakeholders Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, NY, New York
We are seeking an Research Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
US, WA, Seattle
The Data Intelligence team is a new function within Amazon Customer Service (CS). We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences. As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With your expertise in AI, text analysis, embeddings, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage data and AI to drive customer experience improvements, and set new global standards for customer experience. This role requires you to work with a cross-functional team, including scientists, engineers, and product managers, to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technical skills in AI techniques (e.g., automated reasoning, reasoning, planning, knowledge representation), excellent written documentation skills, and experience with big data technologies. Success in this role requires combining deep business knowledge with hands-on technical skills to solve customer problems and address complex technical challenges. Key job responsibilities - Develop innovative solutions to complex problems (e.g., Automated Reasoning for Trusted AI-Enabled Customer Service). - Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers. - Analyze data and define metrics to identify actionable insights and measure improvements in customer experience. - Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal/external publications. - Collaborate with product management and engineering teams to integrate and optimize models in production systems. A day in the life A typical day as an Applied Scientist in the Data Intelligence team involves combining business expertise with hands-on problem-solving in ML and AI. The role encompasses tackling complex data initiatives, ensuring alignment with customer needs and business objectives, and translating business requirements into practical AI-driven solutions. Working collaboratively with cross-functional teams, this position involves designing and enhancing AI models, focusing on efficiency, precision, and scalability. Daily activities include ensuring data quality, monitoring model performance, and generating actionable insights from vast amounts of information. Each day presents opportunities to resolve complex technical challenges, advance important AI projects, and conceive innovative ways to leverage data in transforming the customer experience. About the team The Data Intelligence team is a new function within Amazon Customer Service. We develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), and Natural Language Processing (NLP) techniques to enhance customer service associate and customer experiences.
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazonwe leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Collaborate with simulation and robotics experts to translate physical modeling needs into robust, scalable, and maintainable simulation solutions. - Design and implement high-performance simulation modeling and tools for rigid and deformable body simulation. - Identify and optimize performance bottlenecks in simulation pipelines to support real-time and batch simulation workflows. - Help build validation and unit testing pipelines to ensure correctness and physical fidelity of simulation results. - Identify potential sources of sim-to-real gaps and propose modeling and numerical approximations to reduce them. - Stay current with the latest advances in numerical methods, parallel computing, and GPU architectures, and incorporate them into our tools.
US, MA, North Reading
robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: Enable unprecedented generalization across diverse tasks Enable unprecedented robustness and reliability, industry-ready Integrate multi-modal learning capabilities (visual, tactile, linguistic) Accelerate skill acquisition through demonstration learning Enhance robotic perception and environmental understanding Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Science Manager in the Foundation Model team, you will build and lead a team that develops and improves machine learning systems that help robots perceive, reason, and act in real-world environments. You will set the technical direction for leveraging state-of-the-art models (open source and internal research), evaluating them on representative tasks, and adapting/optimizing them to meet robustness, safety, and performance needs. You will drive the capability roadmap and the evaluation strategy that defines “what the robot brain can do,” and you will sponsor targeted innovation when gaps remain. You’ll collaborate closely with research, controls, hardware, and product teams, and ensure the team’s outputs can be further customized and deployed by downstream teams on specific robot embodiments.
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the nextlevel. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Research Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. At the intersession of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.
US, MA, North Reading
How should a robot dig a single item out of a cluttered bin, feel when it has made contact, and adjust in real time without crushing what it touches? Single- and dual-arm contact-rich manipulation at production scale is still being solved. We are looking for a Principal Applied Scientist to define the control architectures that our next-generation grasping systems will be built on. The Manipulation Robotics team develops robotic workcells to pick, grasp, and move millions of items and packages across Amazon's fulfillment network. Reaching into clutter and grasping deformable and diverse items depends on solving contact: force-aware control, compliant behaviors, and the layering of classical controllers and learned policies. You will set the technical direction for how the team approaches contact-rich manipulation, working with leaders across the organization to establish this foundational capability. Key job responsibilities - Own the technical direction and contact-control strategy for contact-rich manipulation, including the boundary between low-level control and learned policy, balancing tradeoffs among speed, performance, quality, cost, complexity, and adaptability. - Identify the open scientific problems that gate contact-rich manipulation at production scale, and invent the methods that solve them. - Architect contact control built on force and tactile sensing, covering compliant-contact behaviors, hybrid position and force control, and the safe interaction envelope. Stay hands-on and personally write critical-path code. - Partner with scientists, hardware designers, and systems engineers to treat the platform, arm, and end-effector as one coherent hierarchical system, including co-designing compliant end-effectors as part of the control strategy. - Create mechanisms to learn from fielded production, reasoning rigorously about failure modes and improving recovery from unexpected contact. - Drive how simulation, analytical models, demonstrations, and real-robot data are used together to turn lab results into scaled warehouse performance. - Establish the reference implementations, evaluation standards, and design review practices that let others build on the architecture and scale your impact without your continuous involvement. - Develop other scientists and engineers through mentorship, technical review, and a leading role in hiring. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team We are a small, high-ownership team building robotic workcells, from early-stage R&D through products fielded 24/7 in Amazon fulfillment centers. We work at the hard edge of grasping: contact-rich tasks, deformable and variable items, and mechanisms that must survive millions of cycles at fleet scale. Our people own problems end to end, and scientists work shoulder to shoulder with mechanical, electrical, software, and controls engineers in a fast-moving, highly empowered environment where research ideas turn into shipped systems.
IN, KA, Bengaluru
Are you passionate about giving customers the richest, most inspiring experience in their shopping journey? Do you like to dive deep to understand how customer-centric solutions drive measurable results? Do you enjoy working closely with the business and software engineers to design rigorous experiments, build the data infrastructure behind them, and translate results into decisions? You are in the right place! Come join our Prime & Marketing Analytics and Science (PRIMAS) team, where your work will directly impact millions of customers. The EU Marketing & Prime organization is looking for a Data Scientist to join the PRIMAS team. This role sits at the intersection of applied statistics and large-scale analytics — you'll design experiments and causal models, and also own the data pipelines, metrics, and reporting infrastructure that make those results usable across the business. The PRIMAS team provides a comprehensive understanding of customer segments, affinities, and lifetime value. We use data science tools and advanced statistical techniques to study customer purchase and engagement behaviors, and generate actionable insights on where, when, and how we deliver products and programs to customers. We help increase customer engagement, sales, and marketing efficiency, and our systems are built entirely in-house on automated large-scale analytics infrastructure. You will design, launch, and measure experiments across marketing channels (SEM/SEO, Affiliates, Display, Social, Mobile, Email, Onsite, etc.), engagement products, and customer segments. You will improve our understanding of customer behavior, run rigorous power and minimum detectable effect (MDE) analyses to size experiments correctly, and build the causal and conversion models that value and target our marketing — then build the pipelines and dashboards that keep those signals flowing reliably to stakeholders and downstream systems. You will work at the forefront of consumer analytics, tackling some of the hardest measurement problems in the industry alongside strong scientists, statisticians, and software engineers. Key job responsibilities 1. Design and implement scalable, statistically rigorous experiments (A/B, geo, holdout, quasi-experiments) to measure marketing incrementality across channels. 2. Perform power analysis and minimum detectable effect (MDE) calculations to determine experiment sample sizes, durations, and design trade-offs before launch. 3. Build causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems. 4. Building the ETL, metric definitions, and datasets that make results scalable, extensible, and repeatable rather than one-off analyses. 5. Develop measurement frameworks that quantify the true, platform-independent contribution of marketing over time, and build the dashboards and reporting that keep those metrics visible to the business. 6. Apply statistical, mathematical, and machine learning techniques to solve ambiguous business problems where the right approach isn't obvious. 7. Analyze experiment results for validity — inspecting distributions, checking for sample ratio mismatch, exploring covariate balance, and tracking down the source of anomalies. 8. Communicate experiment design, results, and trade-offs clearly to business and leadership audiences, including inputs into business reviews, and influence decisions and technical direction across teams. 9. Establish scalable, repeatable processes and best practices for experiment design, data modeling, and analysis.