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18,486 results found
  • Noah Shutty, Christopher Chamberland
    Physical Review Applied
    2022
    Universal fault-tolerant quantum computers will require the use of efficient protocols to implement encoded operations necessary in the execution of algorithms. In this work, we show how SMT solvers can be used to automate the construction of Clifford circuits with certain fault-tolerance properties and we apply our techniques to a fault-tolerant magic-state-preparation protocol. Part of the protocol requires
  • Miguel Bello, Mónica Benito, Martin J. A. Schuetz, Gloria Platero, Géza Giedke
    Physical Review Applied
    2022
    We propose a protocol for the deterministic generation of entanglement between two ensembles of nuclear spins surrounding two distant quantum dots. The protocol relies on the injection of electrons with definite polarization, their sequential interaction with the nuclear ensembles of each quantum dot for a short time, and the coherent transfer of each electron from one quantum dot to the other. Computing
  • Mario Berta, Marco Tomamichel
    2022 IEEE International Symposium on Information Theory (ISIT)
    2022
    Divergence chain rules for channels relate the divergence of a pair of channel inputs to the divergence of the corresponding channel outputs. An important special case of such a rule is the data-processing inequality, which tells us that if the same channel is applied to both inputs then the divergence cannot increase. Based on direct matrix analysis methods, we derive several Rényi divergence chain rules
  • Hengjiang Ren, Tirth Shah, Hannes Pfeife, Christian Brendel, Vittorio Perera, Florian Marquardt , Oskar Painter
    Nature Communications
    2022
    Light is a powerful tool for controlling mechanical motion, as shown by numerous applications in the field of cavity optomechanics. Recently, small scale optomechanical circuits, connecting a few optical and mechanical modes, have been demonstrated in an ongoing push towards multi-mode on-chip optomechanical systems. An ambitious goal driving this trend is to produce topologically protected phonon transport
  • Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
    Science
    2022
    Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required
  • S. Ebadi, A. Keesling, M. Cain, T. T. Wang, Harry Levine, D. Bluvstein, G. Semeghini, A. Omran, J.-G. Liu, R. Samajdar, X.-Z. Luo, B. Nash, X. Gao, B. Barak, E. Farhi, S. Sachdev, N.Gemelke, L. Zhou, S. Choi, H. Pichler, S.-T. Wang, M. Greiner, V. Vuletic, M. D. Lukin
    Science
    2022
    Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial dimensions, we experimentally investigate quantum algorithms for solving the maximum independent set problem. We use a hardware-efficient encoding associated with Rydberg blockade, realize closed-loop optimization
  • Christopher Chamberland, Earl Campbell
    Physical Review Research
    2022
    Lattice surgery is a measurement-based technique for performing fault-tolerant quantum computation in two dimensions. When using the surface code, the most general lattice surgery operations require lattice irregularities called twist defects. However, implementing twist-based lattice surgery may require additional resources, such as extra device connectivity, and could lower the threshold and overall performance
  • Cleaven Chia, Bart Machielse, Amirhassan Shams-Ansari, Marko Lončar
    Optics Express
    2022
    Diamond offers good optical properties and hosts bright color centers with long spin coherence times. Recent advances in angled-etching of diamond, specifically with reactive ion beam angled etching (RIBAE), have led to successful demonstration of quantum photonic devices operating at visible wavelengths. However, larger devices operating at telecommunication wavelengths have been difficult to fabricate
  • Alkim Bozkurt, Chaitali Joshi, Mohammad Mirhosseini
    Optics Express
    2022
    Optomechanical crystals provide coupling between phonons and photons by confining them to commensurate wavelength-scale dimensions. We present a new concept for designing optomechanical crystals capable of achieving unprecedented coupling rates by confining optical and mechanical waves to deep sub-wavelength dimensions. Our design is based on a dielectric bowtie unit cell with an effective optical/mechanical
  • Qian Xu, Harald Putterman, Joseph Iverson, Kyungjoo Noh, Oskar Painter
    Quantum Computing, Communication, and Simulation II; Series Proceedings of SPIE
    2022
    Stabilized cat qubits that possess biased noise channel with bit-flip errors exponentially smaller than phase-flip errors. Together with a set of bias-preserving (BP) gates, cat qubits are a promising candidate for realizing hardware efficient quantum error correction and fault-tolerant quantum computing. Compared to dissipatively stabilized cat qubits, the Kerr cat qubits can in principle support faster
  • NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)
    2022
    We study the problem of differentially private (DP) fine-tuning of large pre-trained models — a recent privacy-preserving approach suitable for solving downstream tasks with sensitive data. Existing work has demonstrated that high accuracy is possible under strong privacy constraint, yet requires significant computational overhead or modifications to the network architecture. We propose differentially private
  • Henrique Silvério, Sebastián Grijalva, Constantin Dalyac, Lucas Leclerc, Peter Karalekas, Nathan Shammah, Mourad Beji, Louis-Paul Henry, Loïc Henriet
    Quantum
    2022
    Programmable arrays of hundreds of Rydberg atoms have recently enabled the exploration of remarkable phenomena in many-body quantum physics. In addition, the development of high-fidelity quantum gates are making them promising architectures for the implementation of quantum circuits. We present here Pulser, an open-source Python library for programming neutral-atom devices at the pulse level. The low-level
  • Sam McArdle, Earl Campbell, Yuan Su
    Physical Review A
    2022
    Achieving an accurate description of fermionic systems typically requires considerably many more orbitals than fermions. Previous resource analyses of quantum chemistry simulation often failed to exploit this low fermionic number information in the implementation of Trotter-based approaches and overestimated the quantum-computer runtime as a result. They also depended on numerical procedures that are computationally
  • Earl Campbell
    Quantum Science and Technology
    2022
    Simulation of the Hubbard model is a leading candidate for the first useful applications of a fault-tolerant quantum computer. A recent study of quantum algorithms for early simulations of the Hubbard model [Kivlichan et al 2019 Quantum 4 296] found that the lowest resource costs were achieved by split-operator Trotterization combined with the fast-fermionic Fourier transform (FFFT) on an L × L lattice
  • Richard Meister, Simon C. Benjamin, Earl Campbell
    Quantum
    2022
    A highly anticipated use of quantum computers is the simulation of complex quantum systems including molecules and other many-body systems. One promising method involves directly applying a linear combination of unitaries (LCU) to approximate a Taylor series by truncating after some order. Here we present an adaptation of that method, optimized for Hamiltonians with terms of widely varying magnitude, as
  • Harry Levine, Dolev Bluvstein, Alexander Keesling, Tout T. Wang, Sepehr Ebadi, Giulia Semeghini, Ahmed Omran, Markus Greiner, Vladan Vuletić, Mikhail D. Lukin
    Physical Review A
    2022
    Hyperfine atomic states are among the most promising candidates for qubit encoding in quantum information processing. In atomic systems, hyperfine transitions are typically driven through a two-photon Raman process by a laser field which is amplitude modulated at the hyperfine qubit frequency. Here we introduce a method for generating amplitude modulation by phase modulating a laser and reflecting it from
  • Xu Wang, Matthew Wu, Jagan Rajagopalan, Akshay Mohan, DongHyun Kim, Chulsoon Hwang
    IEEE Transactions on Electromagnetic Compatibility
    2022
    Heatsinks may cause radiated emission and radio frequency interference problems when they are mounted on printed circuit boards. In this paper, the radiation mechanism of heatsinks is systematically investigated using characteristic mode theory. The dipole moment is a commonly used equivalent source model for integrated circuits that drive radiated emission from heatsinks. On the basis of a simplified modal
  • Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang
    ACL 2023
    2022
    Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation
  • JSM 2022
    2022
    Measurements of a physical quantity by measuring devices are usually noisy enough that we need to correct, or at least mitigate, the effects of noise. For this purpose, it’s important to distinguish between systematic and random noise since they are of a different nature and independent from each other (when defined properly), so should be dealt with differently. For example, random noise can be significantly
  • Harald Putterman, Joseph Iverson, Qian Xu, Liang Jiang, Oskar Painter
    Physical Review Letters
    2022
    Protected qubits such as the 0-π qubit, and bosonic qubits including cat qubits and Gottesman-Kitaev-Preskill (GKP) qubits offer advantages for fault tolerance. Some of these protected qubits (e.g., 0-π qubit and Kerr-cat qubit) are stabilized by Hamiltonians which have (near-)degenerate ground state manifolds with large energy gaps to the excited state manifolds. Without dissipative stabilization mechanisms
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team. The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation. Key job responsibilities Use statistical and machine learning techniques to create scalable risk management systems Analyzing and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches.
US, NY, New York
Are you passionate about conducting research to develop and grow leaders? Would you like to impact more than 1M Amazonians globally and improve the employee experience? If so, you should consider joining the People eXperience & Technology Central Science (PXTCS) team. Our goal is to be best and most diverse workforce in the world. PXTCS uses science, research, and technology to optimize employee experience and performance across the full employee lifecycle, from first contact through exit. We use economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. This individual should be skilled in core data science tools and methods, icnluding SQL, a statistical software package (e.g., R, Python, or Stata), inferential statistics, and proficient in machine learning. This person should also have strong business acumen to navigate complex, ambiguous business challenges — they should be adept at asking the right questions, knowing what methodologies to use (and why), efficiently analyzing massive datasets, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). In order to move quickly, deliver high-quality results, and adapt to ever-evolving business priorities, effective communication skills in research fundamentals (e.g., research design, measurement, statistics) will also be a must. Major responsibilities will include: - Managing the full life cycle of large-scale research initiatives across multiple business segments that impact leaders in our organization (i.e., develop strategy, gather requirements, manage, and execute) - Serving as a subject matter expert on a wide variety of topics related to research design, measurement, analysis - Working with internal partners and external stakeholders to evaluate research initiatives that provide bottom-line ROI and incremental improvements over time - Collaborating with a cross-functional team that has expertise in social science, machine learning, econometrics, psychometrics, natural language processing, forecasting, optimization, business intelligence, analytics, and policy evaluation - Ability to query and clean complex datasets from multiple sources, to funnel into advanced statistical analysis - Writing high-quality, evidence-based documents that help provide insights to business leaders and gain buy-in - Sharing knowledge, advocating for innovative solutions, and mentoring others Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 1M employees across hundreds of chapters around the world. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth, too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. About the team We are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
The Mission of Amazon's Artificial General Intelligence (AGI) team is to "Build world-class general-purpose intelligence services that benefits every Amazon business and humanity." Are you a data enthusiast? Are you a creative big thinker who is passionate about using data to direct decision making and solve complex and large-scale challenges? If so, then this position is for you! We are looking for a motivated individual with strong analytical and communication skills to join us. In this role, you will apply advanced analytics techniques, AI/ML, and statistical concepts to derive insights from massive datasets. The ideal candidate should have expertise in AI/ML, statistical analysis, and the ability to write code for building models and pipelines to automate data and analytics processing. They will help us design experiments, build models, and develop appropriate metrics to deeply understand the strengths and weaknesses of our systems. They will build dashboards to automate data collection and reporting of relevant data streams, providing leadership and stakeholders with transparency into our system's performance. They will turn their findings into actions by writing detailed reports and providing recommendations on where we should focus our efforts to have the largest customer impact. A successful candidate should be a self-starter, comfortable with ambiguity with strong attention to detail, and have the ability to work in a fast-paced and ever-changing environment. They will also help coach/mentor junior scientists in the team. The ideal candidate should possess excellent verbal and written communication skills, capable of effectively communicating results and insights to both technical and non-technical audiences
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist to work on methodologies for Generative Artificial Intelligence (GenAI) models. As an Applied Scientist, you will be responsible for supporting the development of novel algorithms and modeling techniques to advance the state of the art. Your work will directly impact our customers and will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multi-modal Large Language Models (LLMs) and GenAI. You will have significant influence on our overall strategy by working at the intersection of engineering and applied science to scale pre-training and post-training workflows and build efficient models. You will support the system architecture and the best practices that enable a quality infrastructure. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Pre-training and post-training multimodal LLMs - Scale training, optimization methods, and learning objectives - Utilize, build, and extend upon industry-leading frameworks - Work with other team members to investigate design approaches, prototype new technology, scientific techniques and evaluate technical feasibility - Deliver results independently in a self-organizing Agile environment while constantly embracing and adapting new scientific advances About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Principal Applied Scientist with a strong deep learning background, to lead the development of industry-leading technology with multimodal systems. As a Principal Applied Scientist, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically strong and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).
US, NY, New York
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 extreme. 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. 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. Key job responsibilities - Lead and execute complex, ambiguous research projects from ideation to production deployment - Drive technical strategy and roadmap decisions for ML/AI initiatives - Collaborate cross-functionally with product, engineering, and business teams to translate research into scalable products - Publish research findings at top-tier conferences and contribute to the broader scientific community - Establish best practices for ML experimentation, evaluation, and deployment
US, NY, New York
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 extreme. 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. 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. Key job responsibilities - Lead and execute complex, ambiguous research projects from ideation to production deployment - Drive technical strategy and roadmap decisions for ML/AI initiatives - Collaborate cross-functionally with product, engineering, and business teams to translate research into scalable products - Publish research findings at top-tier conferences and contribute to the broader scientific community - Establish best practices for ML experimentation, evaluation, and deployment
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As a Sr Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.
US, CA, Sunnyvale
As a Principal Applied Scientist within the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions, set the standard for scientific excellence, and make decisions that affect the way we build and integrate algorithms. A Principal Applied Scientist will solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader; develop solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility; and tackle intrinsically hard problems acquiring expertise as needed. Principal Applied Scientists are expected to decompose complex problems into straightforward solutions. You will amplify your impact by leading scientific reviews within your organization or at your location; and scrutinize and review experimental design, modeling, verification and other research procedures. You will also probe assumptions, illuminate pitfalls, and foster shared understanding; align teams toward coherent strategies; and educate keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. AGI Principal Applied Scientists help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, inventing new machine learning techniques, conducting rigorous experiments, and ensuring that research is translated into practice. You will also develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. A Principal Applied Scientist will participate in organizational planning, hiring, mentorship and leadership development. You will build scalable science and engineering solutions, and serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).
US, CA, Sunnyvale
Our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services .