Five ways the ABACUS label advances nature-based carbon removal

From more-accurate measurement of carbon dioxide removal to greater diversity in restoration design, the ABACUS label’s requirements help advance the integrity of restoration projects in the voluntary carbon market.

Amazon cofounded the Climate Pledge in 2019 to commit to reaching net-zero carbon by 2040. The first priority of the pledge is to implement decarbonization strategies — in line with the Paris Agreement — through operational changes such as improving efficiency, driving forward scalable carbon-free energy sources, reducing waste, and innovating materials.

However, alongside real business change that directly reduces greenhouse gas emissions, there is also need for large-scale investments in climate change mitigation outside of our value chain (what we call carbon neutralization). At Amazon, we do this through both nature-based solutions and technological carbon dioxide removal.

Nature-based carbon removal harnesses the power of photosynthesis to sequester carbon in natural and managed ecosystems. This means altering land management in alignment with nature through native reforestation, agroforestry, and other forms of high-quality restoration. These activities alone have the potential to remove 2–4 billion tons of carbon per year; that’s almost half of the estimated 5–10 billion tons per year that experts estimate is likely needed through the end of the century in order to keep our global temperatures at safe levels.

While the voluntary carbon market has the potential to bring billions of dollars of finance to restoration projects, less than 3% of credits issued to date come from nature-based carbon removal. This is due to the voluntary carbon market’s prices’ falling below the costs of high-quality nature-based restoration.

That’s where ABACUS comes in. ABACUS is a set of principles and requirements, codified within Verra’s Verified Carbon Standard, that helps advance the integrity of restoration projects within the voluntary carbon market. ABACUS was developed by a working group of expert practitioners, conservation professionals, and scientists — including Amazon’s own carbon neutralization scientists — in an effort to raise the quality bar for agroforestry and native-restoration projects. The ABACUS label has already begun to raise the quality bar for leading buyers.

Below are five big ideas within ABACUS that help raise the bar on scientific rigor and transparency.

  1. Dynamic baseline to measure additionality

    Historically, restoration carbon projects assume that whatever land use was occurring before a project takes place — pasture or agriculture, for example — would have continued unaltered without the project intervention. This assumption ignores the myriad ecological, economic, and policy dynamics that could affect carbon removal without assistance from the voluntary carbon market.

    Related content
    Investing in 500+ solar and wind projects, bringing carbon-free energy to dirty grids, and buying Renewable Energy Certificates all played a role.

    In addition to demonstrating that a project would not be viable without carbon credit finance, ABACUS requires a treatment-control approach to measuring additionality, or the carbon removal resulting from the project above and beyond what would have occurred otherwise. This means matching the project “treatment” area — based on historical, satellite-based proxies for biomass — to a population of “control” plots that are followed through time. Each of these controls represents a potential alternate reality for the project in the absence of restoration.

    If the control plots regain forest carbon at pace with the project, this indicates that the project may have regained forest carbon on its own, without the intervention. If the control plots remain low-carbon, degraded land, we can be more confident that the project’s climate impacts are additional. By treating additionality as dynamic instead of static, we’re able to obtain a more data-driven estimation of the true impact of restoration.

  2. Carbon projects as engines for agricultural production

    Carbon removal cannot come at the expense of food production; in fact, these challenges are inextricably linked. Under some projections, agricultural production will need to double by 2050, even as the least productive pasture and croplands are restored to forest cover. Sustainably intensifying agriculture to increase food production, while sparing land for carbon removal — or, better, integrating carbon removal within productive agricultural systems — is critical to reconciling these needs.

    Drone footage of a mature cocoa, coconut, and mahogany agroforestry system, adjacent to a degraded pasture in southeast Pará, Brazil.
    ABACUS seeks to restore degraded pasturelands to diverse agroforestry systems like this one. (Drone footage courtesy of Eric Plançon)

    But the voluntary carbon market is not equipped to tackle this challenge. Carbon removal projects that displace agricultural production often result in indirect land use change and associated emissions, as agricultural markets replace lost production to serve growing demand (“leakage”).

    These crop- and region-specific leakage effects are difficult to quantify reliably. Conventional leakage methodologies impose standardized deductions based on default carbon leakage rates when agricultural production is displaced. This creates a persistent source of uncertainty and risk of over-crediting, and the approach misses an opportunity to build synergies between restoration and agricultural production.

    Related content
    From investing in new carbon-free energy projects to advocating for grid modernization and collaborating with key stakeholders around the world, Amazon is working toward a cleaner energy future.

    ABACUS instead takes a “food-forward” approach to leakage accounting. Rather than using an imprecise default value to quantify leakage effects, ABACUS requires projects to eliminate leakage by maintaining or enhancing agricultural production in the project areas and surrounding landscapes. By recognizing the land-sparing effect of enhancing production of different types of commodities, ABACUS encourages projects to co-optimize for carbon and agricultural production and avoids locking regions into specific agricultural products. The working group is engaging partners to create commodity-specific leakage metrics based on land-carbon “opportunity costs” to estimate, and mitigate, the impacts of leakage.

  3. Abbreviated crediting periods for durability assurance

    Carbon stored in ecosystems can be highly durable, but it faces persistent, long-term climate risks such as fire, drought, and land use change, which must be responsibly managed. Nature-based carbon removal should seek “effective permanence” — an actual net greenhouse gas benefit to the atmosphere that is equal to, or greater than, the net benefit represented by the credits. In addition, the removal should ensure that this balance can be maintained indefinitely.

    On the other hand, agroforestry and restoration projects can catalyze shifts to land use systems that durably enhance carbon storage even beyond what is credited. This can happen through spillover effects, continued carbon removal after the crediting period, and biophysical cooling feedbacks, among other factors. ABACUS includes several methods that improve the likelihood that nature-based carbon remains durably stored — for example, requiring projects to plant ecologically appropriate restoration systems and to create public plans for the longevity of project activities even after the support of carbon revenues.

    Related content
    Amazon teams up with RTI International, Schlumberger, and International Paper on a project selected by the US Department of Energy to scale carbon capture and storage for the pulp and paper industry.

    One of ABACUS’s key innovations is to limit the crediting period in an effort to maximize uncredited removals. The ABACUS working group found that revenues from credits generated beyond year 30 are mostly immaterial to investment decisions today, due to their heavy discounts. By shortening the crediting period to 40 years maximum — as opposed to as much as 100 years under some voluntary carbon market standards — ABACUS will create a source of uncredited carbon removal that can serve as an additional buffer against future reversals.

    Additionally, ABACUS proposes that projects will be required to allocate a portion of carbon credits issued late in the crediting period (i.e., years 31–40) to a “long-term permanence mechanism” such as an enhanced buffer pool or insurance product. Achieving increased confidence in the effective permanence of nature-based carbon credits may require stringing together removals or replacing a moderate-durability credit with a high-durability credit, if and when previously credited removals are reversed. Economically, such a construct is currently likely to be cost effective compared to today’s high-durability carbon dioxide removal.

  4. Going beyond commercial monoculture plantations

    Forest plantations already cover nearly 300 million hectares globally — roughly equivalent to the entire area of India. That figure has more than doubled in the last 30 years, without a robust voluntary carbon market, and it is projected to continue growing to provide timber, pulpwood, firewood, and charcoal to increasing populations and a growing economy.

    Brazil_Drone.png
    Orthorectified mosaic capturing a range of land management types on a typical farm in the Amazon basin, Brazil. We can see the contrast between low-carbon-density pasture (left) and diverse agroforestry (center), which combines shade-tolerant commodity production with native, carbon-rich hardwood trees. ABACUS is designed to support native restoration and agroforestry interventions on formerly forested, degraded land.
    Photos captured and combined by ICRAF-Brazil on behalf of the Agroforestry Accelerator.

    As a first step, ABACUS prohibits most monocultures and requires project developers to use observed or modeled data to demonstrate that planted systems are ecologically appropriate for the landscape. This approach avoids projects seeking to reforest with systems that aren’t suitable for the location’s native biomass potential — a function of climate, soil type, water availability, and elevation, among other things. Credit buyers are encouraged to send demand signals that further encourage biodiverse, ecologically sound, and socially beneficial restoration.

  5. Transparency to foster competition on quality

    For some aspects of restoration, it’s challenging to prescribe universally applicable requirements without stifling innovation and local knowledge: every restored ecosystem is unique in its own way. ABACUS introduces multiple requirements for added transparency that will allow buyers, investors, and the public to better assess for themselves the effectiveness of project designs and measurement.

    Related content
    Amazon advocates for updating carbon accounting to measure where renewable-energy projects will have the greatest impact.

    For example, ABACUS projects will need to publish their in-situ inventory measurements, systematically justify their use of allometric or other scaling models, and report on design approaches to avoid measurement or sampling bias. Instead of once every five years or so, ABACUS requires projects to annually map disturbances, to ensure that carbon credited and subsequently reversed is immediately identified. With enhanced transparency, the ABACUS working group hopes to incentivize project developers to compete on quality.

  6. ABACUS doesn’t solve all of the challenges of quantifying the complete climate impact of nature-based carbon removal, and it is no replacement for the stakeholder engagement necessary to ensure genuine socio-economic benefits on the ground. Many important improvements remain for future versions of the label’s principles and requirements. As we learn, the ABACUS working group will continue to enhance the scientific rigor of and public confidence in ecosystem restoration, catalyzing rural restoration economies and livelihoods and — if we succeed — helping to enable billions of tons of ecosystem carbon removal across the world.

Research areas

Related content

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 AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - 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 the field; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.
US, NY, New York
We are seeking an Applied 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
IN, KA, Bangalore
Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 3+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems
CN, 11, Beijing
北京职位 - 如果希望在北京工作,请投递本职位。 毕业时间:2026年10月 - 2027年9月之间毕业的应届毕业生 · 投递须知: 1 填写简历申请时,请把必填和非必填项都填写完整。提交简历之后就无法修改了哦! 2 学校的英文全称请准确填写。中英文对应表,请点击链接查看 https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2 3 简历不限中英文。 如果您正在攻读计算机,AI,ML等相关专业的博士或硕士研究生,而且对应用科学家的工作感兴趣。如果您也喜爱深入研究棘手的技术问题并提出解决方案,用成功的产品显著地改善人们的生活。 那么,我们诚挚邀请您加入亚马逊的International Technology搜索团队改善Amazon的产品和服务。我们的目标是帮助亚马逊的客户找到他们所需的产品,并发现他们感兴趣的新产品。这会是一份收获满满的工作。您每天的工作都与全球数百万亚马逊客户的体验紧密相关。您将提出创新,基于TB级别的产品和流量数据设计模型。您将集成这些模型为客户提供服务,通过数据,建模和客户反馈来完成闭环。您对模型的选择需要能够平衡业务指标和响应时间的需求。 Key job responsibilities 基本要求 正在攻读计算机工程,计算机科学,AI,机器学习,运筹学,统计或相关领域的硕士或博士学位。 有AI,ML实验设计和统计分析的经验。 有使用代码和工具实现算法的经验。 至少能熟练地使用一种编程语言(例如Python,Java,C ++等)编码和解决问题。 加分项: 在顶级会议或者期刊上有论文发表。 技术视野好;能与技术团队成员深入讨论概念和算法,并对应用问题提出恰当的解决方案。 出色的批判性思维能力;能以口头和书面形式清楚地向团队沟通您的技术方案。
GB, London
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 200 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. PV observability team's mission is to deliver efficient, zero-touch observability solutions that combine log management, tracing, and AI-powered analytics, enabling teams to detect, diagnose, and resolve Prime Video issues at unprecedented speed. We are looking for an Applied Scientist for our London office experienced in generative AI and large models. This is a wide impact role working with development teams across the UK, India, and the US. You will develop and deploy customized models for PV builders needs at scale, and explore emerging techniques that help us make better decisions faster for agentic solutions. This is a hands-on role working with a high performing and high visibility multidisciplinary group of engineers and scientists in the London office, focused on improving the PV builders experience for Prime Video organization. You will have strong technical ability, excellent teamwork and communication skills, and a strong motivation to deliver customer value from your research. Our position offers opportunities to grow your technical and non-technical skills and make a global impact immediately. Key job responsibilities - Develop machine learning algorithms for high-scale recommendations problems - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement - Collaborate with software engineers to integrate successful experimental results into Prime Video wide processes - Report and share results with the team and wider scientific community by authoring documents that are both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment A day in the life You will lead the design of machine learning models that scale to very large quantities of data across multiple dimensions. You will embody scientific rigor, designing and executing experiments to demonstrate the technical effectiveness and business value of your methods. You will work alongside other scientists and engineering teams to deliver your research into production systems. About the team Our team owns Prime Video observability features for development teams. We consume PBs of data daily which feed into multiple observability features focussed on reducing the customer impact time.
US, CA, Santa Clara
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Santa Clara, California Job Number: AMZ9976173 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range: $183,000/year to $247,600/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, CA, Sunnyvale
We are looking for a Principal Applied Scientist to drive the research and development of real-time multimodal conversational AI. You will operate across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will be the expert in your area while contributing across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what's possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Principal Scientist, you will set the technical direction for your research area, influence the broader roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment from inception. Key job responsibilities Foundation Model Scaling - Build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational modes, including the relationship between data, model size, and real time performance. - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation Real-Time Perception & Generation - Advance the team's capabilities in real-time perception - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets
US, WA, Bellevue
The Amazon Fulfillment Technologies (AFT) Science team is looking for an exceptional Applied Scientist, with strong optimization and analytical skills, to develop production solutions for one of the most complex systems in the world: Amazon’s Fulfillment Network. At AFT Science, we design, build and deploy optimization, simulation, and machine learning solutions to power the production systems running at world wide Amazon Fulfillment Centers. We solve a wide range of problems that are encountered in the network, including labor planning and staffing, demand prioritization, pick assignment and scheduling, and flow process optimization. We are tasked to develop innovative, scalable, and reliable science-driven solutions that are beyond the published state of art in order to run frequently (ranging from every few minutes to every few hours per use case) and continuously in our large scale network. Key job responsibilities As an Applied Scientist, you will work with other scientists, software engineers, product managers, and operations leaders to develop scientific solutions and analytics using a variety of tools and observe direct impact to process efficiency and associate experience in the fulfillment network. Key responsibilities include: * Develop an understanding and domain knowledge of operational processes, system architecture and functions, and business requirements * Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approach * Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges * Create prototypes and simulations for agile experimentation of devised solutions * Advocate technical solutions to business stakeholders, engineering teams, and senior leadership * Partner with engineers to integrate prototypes into production systems * Design experiment to test new or incremental solutions launched in production and build metrics to track performance A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. 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 Amazon Fulfillment Technology (AFT) designs, develops and operates the end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FC). We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. The AFT Science team has expertise in operations research, optimization, scheduling, planning, simulation, and machine learning. We also have domain expertise in the operational processes within the FCs and their defects. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment which includes both developing novel solutions or improving existing approaches. Resulting production systems rely on a diverse set of technologies, our teams therefore invest in multiple specialties as the needs of each focus area evolves.
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, OR, Portland
AWS Science of Security is dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty. Key job responsibilities The successful candidate will: * Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. * Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. *Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. * Develop strategic plans to identify fundamentally new solutions for business problems. * Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.