Decarbonizing paper packaging

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.

Amazon delivers billions of packages each year, and for these deliveries, we aim to use as little additional packaging as possible while still ensuring products arrive safely. When additional packaging is necessary, the majority of the packaging materials we use are made from paper. This includes boxes, paper mailers, and in some cases, paper bags. One of the sustainability benefits of using paper over other packaging materials, such as conventional plastics, is that paper is generally easier to recycle for our customers. However, as with any material produced at scale today, the production processes result in carbon emissions.

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The carbon emissions associated with paper packaging depend heavily on the type of paper mill, the processes and fuels used, and the grade of paper products produced. Currently, the most common type of containerboard paper mill in the US is a mixed paper mill that uses both virgin and recycled fiber. The recycled fiber generally comes from “old corrugated containers (OCC)”, which is a recycling stream that includes recycled boxes such as the Amazon boxes recycled by our customers. The virgin fiber is produced from wood chips that go through a chemical pulping process that breaks the bonds between the cellulose fibers in the wood and a glue-like substance called lignin that holds the fibers together. Containerboard paper mills that use both recycled and virgin fiber are the most common because of reduced energy demands, raw-material flexibility, cost effectiveness, and the regulatory environment.

For mills that use at least some virgin fiber to make paper, the process used to make the virgin fiber generates waste biomass that can be used as a fuel to reduce the reliance on fossil fuels. This waste biomass includes wood waste from debarking and chipping the wood and residual lignin (often called black liquor), a byproduct of the pulping process. When this waste biomass is used as a fuel for generating on-site steam and electricity, it generates what is called biogenic carbon emissions. Biogenic emissions are defined as CO2 emissions related to the natural carbon cycle, which include emissions resulting from the combustion of biological materials.

Biogenic emissions from the combustion of biomass release CO2 that was recently (in geological timeframes) sequestered by biological materials, such as plants. The US Environmental Protection Agency (EPA) does not include biogenic emissions in greenhouse gas (GHG) reporting and considers these emissions carbon neutral because of their negligible net contribution to atmospheric CO2 concentrations. This is in contrast to the carbon emissions associated with the burning of fossil fuels, which do contribute to reported GHG emissions.

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In 2021, about 74% of the direct carbon emissions reported by the US pulp and paper sector were biogenic, with the remaining emissions coming from the use of fossil fuels. If these biogenic emissions are captured and permanently stored, instead of being released to the atmosphere, it enables the production of lower-carbon paper compared to traditional methods. By capturing and permanently storing biogenic carbon emissions, it is technically possible to sequester more biogenic carbon than the amount of fossil-based carbon released to the atmosphere as a result of the various industrial processes associated with paper production. This approach is generally referred to as bioenergy with carbon capture and storage (BECCS) and is considered by the Intergovernmental Panel on Climate Change (IPCC) as one of the key carbon dioxide removal technologies needed to limit global warming to 1.5°C above pre-industrial levels.

Stylized representations of a forest, cut logs, a paper mill, an energy plant, and decarbonized packaging boxes, showing (1) carbon flowing from the atmosphere to the forest and logs; (2) pulpwood, recycled boxes, and steam and electricity from the energy plant passing to the paper mill; (3) black liquor from the paper mill and woodwaste from the pulping process flowing to the energy plant; and sequestered carbon from the energy plant being stored underground.
Carbon flows for the production of paper with integrated carbon capture and storage.

Decarbonized paper packaging

Carbon capture and storage (CCS) technology, along with sustainably managed forests, together have the potential to create a paper industry that becomes a climate solution by sequestering more emissions than the industry is responsible for releasing to the atmosphere. Although CCS technologies show promise for helping reduce or even eliminate GHG emissions, these technologies have not yet been proven out at a larger scale.

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To help accelerate the development and adoption of CCS, we assembled a multi-disciplinary team to develop and propose a concept to build a CCS plant at a containerboard mill operated by one of our packaging suppliers. The proposal was one of only four selected by the Office of Clean Energy Demonstrations in the US Department of Energy (DOE). Our team includes International Paper (IP), Schlumberger (SLB) for the design and engineering, and RTI International, the research organization that originally developed the carbon capture technology. RTI took the lead on the proposal submission.

The award is for up to $88 million, and if we are able to successfully complete all stages of this first-of-its-kind project (target date 2029), this large-scale demonstration facility will capture up to 120,000 metric tons of CO2 per year, a portion of which are biogenic emissions. A CCS plant of this size can enable an annual production of approximately 100,000 metric tons of decarbonized paper that can be used for future Amazon boxes and other packaging, benefiting both our customers and the environment.

Data on a virgin mill, a virgin-and-recycled mill, and a recycled mill, showing that, in all three, bioenergy with carbon capture and storage reduces carbon emissions significantly more than conventional carbon capture and storage.
This chart shows the impact of electrification, fuel switching, the use of bioenergy, and CCS on the cradle-to-gate greenhouse gas (GHG) emissions of containerboard paper produced from a virgin mill, mixed paper mill, and recycled mill. The analysis was performed using an internal Amazon decarbonization model based on US average data from FisherSolve®.

Carbon capture technologies

Carbon capture technologies absorb and separate CO2 from exhaust gases associated with combustion processes that burn fuels to generate thermal energy. There are three main approaches to how CO2 can be separated from exhaust gases: 1) pre-combustion capture, which involves absorbing the CO2 before the combustion is completed; 2) post-combustion capture, which involves absorbing the CO2 after the combustion process; and 3) oxyfuel combustion capture, which refers to burning the fuels in pure oxygen instead of air. With the oxyfuel approach, the exhaust gas is predominantly composed of CO2 and water vapor, enabling the CO2 to be easily separated. Each of these ways to absorb the CO2 varies in terms of efficacy, energy demand, and cost. The most well-developed approach is post-combustion capture.

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For post-combustion capture, the CO2 can be captured by a liquid or a solid material that likes to bind to CO2. For the liquid approach, a mixture of water and 20-30% of an amine compound is typically used to bind the CO2. The process begins with putting the water-amine mixture in contact with the exhaust gases to selectively absorb the CO2 from the gas stream. After the CO2 has been absorbed, the liquid mixture is heated in a different step to release the CO2. The separated CO2 can then be compressed for storage, and the amine can be regenerated for re-use. This water-amine liquid method is known for its efficacy, but it is also relatively energy intensive. Solid materials have shown equally good adsorption of CO2, but they also require relatively high amounts of energy to adsorb and release the CO2.

Over the past 13 years, RTI International has been developing a different water-amine liquid that has much less water and more amine to address the core challenges with the traditional water-amine solutions. By significantly reducing the water content in the amine-based liquid, RTI’s non-aqueous amine solvent (NAS) carbon capture technology is able to lower the energy requirements for the absorption-regeneration cycle by up to 36% compared to the traditional water-amine liquid.

In addition to reducing energy consumption, the NAS technology also minimizes operational risks and maintenance costs due to its extremely low corrosivity and enhanced physicochemical properties. RTI’s NAS process represents a step forward for industrial decarbonization. The deployment of RTI’s NAS technology through the awarded DOE project at IP’s Vicksburg containerboard mill can help demonstrate the scalability of this promising approach to carbon capture.

Amazon co-founded the Climate Pledge with a goal to reach net-zero carbon emissions across our operations by 2040, 10 years ahead of the Paris Agreement. We recognize that achieving this ambitious goal requires partnerships across all industries to explore and develop cutting-edge carbon reduction technologies, such as CCS. This project allows Amazon to work with one of its paper packaging suppliers to scale up and demonstrate RTI’s NAS technology to decarbonize the papermaking process. This project will also serve as the foundation for de-risking and scaling this technology more broadly in the pulp and paper industry and across other sectors, such as cement and steel.

Research areas

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US, WA, Seattle
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We're out to revolutionize how medicines are discovered, developed, and brought to patients, powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models, large language models, and agentic reasoning systems to life sciences problems, then put them into the hands of pharma, biotech, and diagnostics customers as applications and managed services they can fine-tune, tailor, and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological, regulatory, and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. 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You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that thousands of scientists use daily. 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US, WA, Seattle
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Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Set the long-term scientific vision and team strategy: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap, and build the team and plan to deliver it. Secure buy-in from VP-level leadership. Hire and grow scientific talent: Own recruiting, calibration, development, and retention for the science team. Mentor scientists toward senior and principal scope, and raise the scientific bar across the organization. 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Establish scientific rigor and operational excellence: Set the standard for experimentation, evaluation, and reproducibility, and the mechanisms that keep the science organization productive and accountable. Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. 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US, WA, Seattle
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IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
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We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
IN, KA, Bengaluru
The Ads Trust Science team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.