A line of Amazon packages are seen traveling down a conveyor belt
Amazon associates are always on the lookout for damaged items, but an extra pair of “eyes” may one day support them in this task, powered by machine-learning approaches being developed by Amazon’s Robotics AI team in Berlin, Germany.

The surprisingly subtle challenge of automating damage detection

Why detecting damage is so tricky at Amazon’s scale — and how researchers are training robots to help with that gargantuan task.

With billions of customer orders flowing through Amazon’s global network of fulfillment centers (FCs) every year, it is an unfortunate but inevitable fact that some of those items will suffer accidental damage during their journey through a warehouse.

Amazon associates are always on the lookout for damaged items in the FC, but an extra pair of “eyes” may one day support them in this task, powered by machine-learning approaches being developed by Amazon’s Robotics AI team in Berlin, Germany.

Related content
The customer-obsessed science produced by teams in Berlin is integrated in several Amazon products and services, including retail, Alexa, robotics, and more.

As well as avoiding delays in shipping and improving warehouse efficiency, this particular form of artificial intelligence has the benefit of aiming to reduce waste by shipping fewer damaged goods in the first place, ensuring customers have fewer damaged items to return.

For every thousand items that make their way through an FC prior to being dispatched to the customer, fewer than one becomes damaged. That is a tiny proportion, relatively speaking, but working at the scale of Amazon this nevertheless adds up to a challenging problem.

Damage detection is important because while damage is a costly problem in itself, it becomes even more costly the longer the damage goes undetected.

Amazon associates examine items at multiple occasions through the fulfillment process, of course, but if damage occurs late in the journey and a compromised item makes it as far as the final packaging station, an associate must sideline it so that a replacement can be requested, potentially delaying delivery. As associate must then further examine the sidelined item to determine its future.

Related content
New statistical model reduces shipment damage by 24% while cutting shipping costs by 5%.

Toward the end of 2020, Sebastian Hoefer, senior applied scientist with the Amazon Robotics AI team, supported by his Amazon colleagues, successfully pitched a novel project to address this problem. The idea: combine computer vision and machine learning (ML) approaches in an attempt to automate the detection of product damage in Amazon FCs.

“You want to avoid damage altogether, but in order to do so you need to first detect it,” notes Hoefer. “We are building that capability, so that robots in the future will be able to utilize it and assist in damage detection.”

Needles in a haystack

Damage detection is a challenging scientific problem, for two main reasons.

Damage caused in Amazon FCs is rare, and that’s clearly a good thing. But that also makes it challenging because we need to find these needles in the haystack, and identify the many forms damage can take.
Ariel Gordon

The first reason is purely practical — there is precious little data on which to train ML models.

“Damage caused in Amazon FCs is rare, and that’s clearly a good thing,” says Ariel Gordon, a principal applied scientist supporting Hoefer’s team from Seattle. “But that also makes it challenging because we need to find these needles in the haystack, and identify the many forms damage can take.”

The second reason takes us into the theoretical long grass of artificial intelligence more generally.

For an adult human, everyday damage detection feels easy — we cannot help but notice damage, because our ability to do so has been honed as a fundamental life skill. Yet whether something is sufficiently damaged to render it unsellable is subjective, often ambiguous, and depends on the context, says Maksim Lapin, an Amazon senior applied scientist in Berlin. “Is it damage that is tolerable from the customer point of view, like minor damage to external packaging that will be thrown into the recycling anyway?” Lapin asks. “Or is it damage of a similar degree on the product itself, which would definitely need to be flagged?”

A side by side image shows a perforated white mailer, on the left is a standard image, on the right is the damage as "seen" by Amazon's damage detection models
Damage in Amazon fulfillment centers can be hard to spot, unlike this perforation captured by a standard camera (left) and Amazon's damage detection models (right.)

In addition, the nature of product damage makes it difficult to even define what damage is for ML models. Damage is both heterogenous — any item or product can be damaged — and can take many forms, from rips to holes to a single broken part of a larger set. Multiplied over Amazon’s massive catalogue of items, the challenge becomes enormous.

In short, do ML models stand a chance?

Off to “Damage Land”

To find out, Hoefer’s team first needed to obtain that data in a standardized format amenable to machine learning. They set about collecting it at an FC near Hamburg, Germany, called HAM2, in a section of the warehouse affectionately known as “Damage Land”. Damaged items end up there while decisions are made on whether such items can be sold at a discount, refurbished, donated or, as a last resort, disposed of.

The team set up a sensor-laden, illuminated booth in Damage Land.

“I’m very proud that HAM2 was picked up as pilot site for this initiative,” says Julia Dembeck, a senior operations manager at HAM2, who set up the Damage Taskforce to coordinate the project’s many stakeholders. “Our aim was to support the project wholeheartedly.”

After workshops with Amazon associates to explain the project and its goals, associates started placing damaged items on a tray in the booth, which snapped images using an array of RGB and depth cameras. They then manually annotated the damage in the images using a linked computer terminal.

Annotating damage detection

“The results were amazing and got even better when associates shared their best practices on the optimal way to place items in the tray,” says Dembeck. Types of damage included things like crushes, tears, holes, deconstruction (e.g., contents breaking out from its container) and spillages.

The associates collected about 30,000 product images in this way, two-thirds of which were images of damaged items.

“We also collected images of non-damaged items because otherwise we cannot train our models to distinguish between the two,” says Hoefer. “Twenty thousand pictures of damage are not a lot in ‘big data’ terms, but it is a lot given the rarity of damage.”

With data in hand, the team first applied a supervised learning ML approach, a workhorse in computer vision. They used the data as a labelled training set that would allow the algorithm to build a generalizable model of what damage can look like. When put through its paces on images of products it had never seen before, the model’s early results were promising.

When analyzing a previously unseen image of a product, the model would ascribe a damage confidence score. The higher the score, the more confident it was that the item was damaged.

The researchers had to tune the sensitivity of the model by deciding upon the confidence threshold at which the model would declare a product unfit for sending to a customer. Set that threshold too high, and modest but significant damage could be missed. Set it too low, and the model would declare some undamaged items to be damaged, a false positive.

“We did a back-of-the-envelope calculation and found that if we're sidelining more than a tiny fraction of all items going through this process, then we're going to overwhelm with false positives,” says Hoefer.

Since those preliminary results in late 2021, the team has made significant improvements.

“We’re now optimizing the model to reduce its false positive rate, and our accuracy is increasing week to week,” says Hoefer.

Different types of damage

However, the supervised learning approach alone, while promising, suffers some drawbacks.

For example, what is the model to make of the packaging of a phone protector kit that shows a smashed screen? What is it to make of a cleaning product whose box is awash with apparent spills? What about a blister pack that is entirely undamaged and should hold three razor blades but for some reason contains just two — the “broken set” problem? What about a bag of ground coffee that appears uncompromised but is sitting next to a little puddle of brown powder?

Again, for humans, making sense of such situations is second nature. We not only know what damage looks like, but also quickly learn what undamaged products should look like. We learn to spot anomalies.

Hoefer’s team decided to incorporate this ability into their damage detection system, to create a more rounded and accurate model. Again, more data was needed, because if you want to know what an item should look like, you need standardized imagery of it. This is where recent work pioneered by Amazon’s Multimodal Identification (MMID) team, part of Berlin's Robotics AI group, came in.

The MMID team has developed a computer vision tool that enables the identification of a product purely from images of it. This is useful in cases where the all-important product barcode is smudged, missing, or wrong.

In fact, it was largely the MMID team that developed the sensor-laden photo booth hardware now being put to use by Hoefer’s team. The MMID team needed it to create a gallery of standardized reference images of pristine products.

Related content
A combination of deep learning, natural language processing, and computer vision enables Amazon to hone in on the right amount of packaging for each product.

“Damage detection could also exploit the same approach by identifying discrepancies between a product image and a gallery of reference images,” says Anton Milan, an Amazon senior applied scientist who is working across MMID and damage detection in Berlin. “In fact, our previous work on MMID allowed us to quickly take off exploring this direction in damage detection by evaluating and tweaking existing solutions.”

By incorporating the MMID team’s product image data and adapting that team’s techniques and models to sharpen their own, the damage-detection system now has a fighting chance of spotting broken sets. It is also much less likely to be fooled by damage-like images printed on the packaging of products, because it can check product imagery taken during the fulfillment process against the image of a pristine version of that product.

“Essentially, we are developing the model’s ability to say ‘something is amiss here’, and that’s a very useful signal,” says Gordon. “It's also problematic, though, because sometimes products change their design. So, the model has to be ‘alive’, continuously learning and updating in accordance with new packaging styles.”

The team is currently exploring how to combine the contributions of both discriminative and anomaly-based ML approaches to give the most accurate assessment of product damage. At the same time, they are developing hardware for trial deployment in an FC, and also collecting more data on damaged items.

The whole enterprise has come together fast, says Hoefer. “We pitched the idea just 18 months ago, and already we have an array of hardware and a team of 15 people making it a reality. As a scientist, this is super rewarding. And if it works as well as we hope, it could be sitting in across the network of Amazon fulfillment centers within a couple of years.”

Hoefer anticipates that the project will ultimately improve customer experience while also reducing waste.

Related content
Amazon Lab126 and the Center for Risk and Reliability will study how devices are accidentally damaged — and how to help ensure they survive more of those incidents.

“Once the technology matures, we expect to see a decrease in customer returns due to damage, because we will be able to identify and fix damaged products before dispatching them to customers. Not only that, by identifying damage early in the fulfillment chain, we will be able to work with vendors to build more robust products. This will again result in reducing damage overall — an important long-term goal of the project,” says Hoefer.

Also looking to the future, Lapin imagines this technology beyond warehousing.

“We are building these capabilities for the highly controlled environments of Amazon fulfillment centers, but I can see some future version of it being deployed in the wild, so to speak, in more chaotic bricks-and-mortar stores, where customers interact with products in unpredictable ways,” says Lapin.

Related content

GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. 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 large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
US, WA, Seattle
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! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
IL, Tel Aviv
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience 3+ years of building models for business application experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
IL, Haifa
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.
US, NY, New York
Join us in a historic endeavor to make Generative AI accessible to the world with breakthrough research! The AWS AI team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists drives the innovation that enables external and internal SageMaker customers to train their next generation models on both GPU and Trainium instances. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and 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, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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.
US, WA, Seattle
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 team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - 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 Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.