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

US, VA, Arlington
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services. About AWS Diverse Experiences AWS 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 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. 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. Mentorship & 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. 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN
US, VA, Arlington
Device Economics is looking for an economist experienced in causal inference, empirical industrial organization, forecasting, and scaled systems to work on business problems to advance critical resource allocation and pricing decisions in the Amazon Devices org. Output will be included in scaled systems to automate existing processes and to maximize business and customer objectives. Amazon Devices designs and builds Amazon first-party consumer electronics products to delight and engage customers. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sale both online and in offline retailers in several regions. The space becomes more complex with dynamic product offering with new product launches and new marketplace launches. The Device Economics team leads in analyzing these complex marketplace dynamics to enable science-driven decision making in the Devices org. Device Economics achieves this by combining economic expertise with macroeconomic trends, and including both in scientific applications for use by internal analysts, to provide deep understanding of customer preferences. Our team’s outputs inform product development decisions, investments in future product categories, product pricing and promotion, and bundling across complementary product lines. We have achieved substantial impact on the Devices business, and will achieve more. Device Economics seeks an economist adept in measuring customer preferences and behaviors with proven capacity to innovate, scale measurement, and drive rigor. The candidate will work with Amazon Devices science leadership to refine science roadmaps, models, and priorities for innovation and simplification, and advance adoption of insights to influence important resource allocation and prioritization decisions. Effective communication skills (verbal and written) are required to ensure success of this collaboration. The candidate must be passionate about advancing science for business and customer impact. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Seattle, WA, USA
US, TX, Austin
Passionate about helping customers simplify and accelerate their workloads on AWS? Are you fascinated by the power of Large Language Models (LLM), and applying ML and AI to solve complex challenges within one of Amazon's most significant businesses. Come join the AWS Marketplace team and help us build the future of partnerships and marketplace through ML. You will be a part of a team consisting of experienced Applied Scientists working on a new set of initiatives, building models and delivering them into the Amazon production ecosystem. Your efforts will build a robust ensemble of LLM artifacts and ML systems that can achieve high precision and recall, and scale to new marketplaces and languages. This problem is challenging due to sheer scale (billions of dollars of AWS revenue impact), diversity (hundreds of AWS services) and multitude of input sources (thousands of Partners and millions of Partner activity). We are looking for an experienced Scientist who can develop best in class solutions. Your primary customers are AWS customers and AWS Partners who would thank you for accelerating their migration to the AWS cloud across countries and industries. Inclusive Team Culture Our team is intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. We’re looking for a new teammate who is enthusiastic, empathetic, curious, motivated, reliable, and able to work effectively with a diverse team of peers; someone who will help us amplify the positive & inclusive team culture we’ve been building. Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance We understand that sometimes you need to work from home. Outside of quarantine, our team is co-located across our New York and Seattle offices. We work from home as needed for doctors appointments, to take our kids to school, pick them up, and do the things that can’t be done from the office. Mentorship & Career Growth We are dedicated to supporting new team members. Our team has a broad mix of experience levels and Amazon tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. You'll have an opportunity to learn from people with and without engineering and science backgrounds through one-on-one mentoring, and helpful code/design reviews. As a Senior Applied Scientist, you'll actively mentor others on the team on the art of what's possible with Machine Learning. Key job responsibilities The ideal Applied Scientist candidate has deep expertise in one or several of the following fields: Web search, Applied/Theoretical Machine Learning, Large Language Models, Deep Neural Networks, Classification Systems, Clustering, Label Propagation, Natural Language Processing, Computer Vision, Active learning, and Artificial Intelligence. S/he has a strong publication record at top relevant academic venues and experience in launching products/features in the industry. Do you want the excitement of experimenting with cutting edge Large Language Models (LLMs), machine learning, natural language processing, computer vision, and active learning models to solve real world problems at scale? Imagine experimenting with LLMs, with Deep Neural Networks as your daily job and imagine using your model outputs to affect the adoption of AWS cloud at scale through Partners. Imagine doing research inside of an Amazon team that is always looking to deploy creative solutions to real world problems in Cloud migration. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. About the team The AWS Partner Organization (APO) supports tens of thousands of consulting and technology partners who in turn serve AWS customers across nearly every industry, segment, and geography. The APO Engineering team is driven by the mission to provide best partner experience worldwide and create a better future for our customers and communities through a culture of Customer obsession, innovation and a relentless pursuit of excellence. Our team is responsible for both internal and external support for Partner Sales, the APN, and other key elements that drive the AWS partner engagement. Our commitment is to serve three primary stakeholder groups: the partners who collaborate with us to develop, promote, and sell AWS, the customers who rely on AWS services, and our internal users responsible for fostering AWS sales and partner-based adoption. This position is a critical enabler driving intelligence and science roadmaps for this entire organization. We are open to hiring candidates to work out of one of the following locations: Austin, TX, USA | Denver, CO, USA | New York City, NY, USA | Seattle, WA, USA
US, MA, North Reading
Working at Amazon Robotics Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of doers that work passionately to apply cutting-edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Position Overview The Amazon Robotics (AR) Software Research and Science team builds and runs simulation experiments and delivers analyses that are central to understanding the performance of the entire AR system. This includes operational and software scaling characteristics, bottlenecks, and robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment. We are seeking an enthusiastic Data Scientist to design and implement state-of-the-art solutions for never-before-solved problems. The DS will collaborate closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. They will partner with technology and product leaders to solve business problems using scientific approaches. They will build new tools and invent business insights that surprise and delight our customers. They will work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. They will work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. They will engage with software development teams and warehouse design engineers to drive the evolution of the AR system, as well as the simulation engine that supports our work. Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. We have innovative benefit offerings and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. 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! 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! We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA
US, NY, New York
We are looking for a motivated and experienced Data Scientist with experience in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture with deep understanding in advertising businesses, to be part of a team of talented scientists and engineers to innovate, iterate, and solve real world problem with cutting-edge AWS technologies. In this role, you will take a leading role in defining the problem, innovating the ML/AI solutions, and information the tech roadmap. You will join a cross-functional, fun-loving team, working closely with scientists and engineers on a daily basis. You will innovate on behalf of our customers by prototyping, delivering functional proofs of concept (POCs), and partnering with our engineers to productize and scale successful POCs. If you are passionate about creating the future, come join us as we have fun, and make history. Key job responsibilities - Define and execute a research & development roadmap that drives data-informed decision making for marketers and advertisers - Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions - Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale - Partner with product managers and stakeholders to define forward-looking product visions and prospective business use cases - Drive and lead of culture of data-driven innovations within and outside across Amazon Ads Marketing orgs About the team Marketing Decision Science provides science products to enable Amazon Ads Marketing to deliver relevant and compelling guidance across marketing channels to prospective and active advertisers for success on Amazon. We own the product, technology and deployment roadmap for AI- and analytics-powered products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right team to help them make the data-informed advertising decisions. Our science-based products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
DE, Berlin
AWS AI is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology and code generation. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU
US, VA, Arlington
Amazon launched the Generative AI Innovation Center (GenAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GenAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GenAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team About AWS Diverse Experiences AWS 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. 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. 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. Mentorship & 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. 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. Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NY, USA | San Francisco, CA, USA | San Jose, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
US, VA, Arlington
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: - Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries - Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them - Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team About AWS Diverse Experiences AWS 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. 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. 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. Mentorship & 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. 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NJ, USA | New York, NY, USA | San Francisco, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
CN, Shanghai
亚马逊云科技上海人工智能实验室OpenSearch 研发团队正在招募应用科学实习生-多模态检索与生成方向实习生。OpenSearch是一个开源的搜索和数据分析套件, 它旨在为数据密集型应用构建解决方案,内置高性能、开发者友好的工具,并集成了强大的机器学习、数据处理功能,可以为客户提供灵活的数据探索、丰富和可视化功能,帮助客户从复杂的数据中发现有价值的信息。OpenSearch是现有AWS托管服务(AWS OpenSearch)的基础,OpenSearch核心团队负责维护OpenSearch代码库,他们的目标是使OpenSearch安全、高效、可扩展、可扩展并永远开源。 点击下方链接查看申请手册获得更多信息: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 在这个实习期间,你将有机会: 1. 研究最新的搜索相关性人工智能算法。 2. 探索大模型技术在数据分析与可视化上的应用。 3. 了解主流搜索引擎Lucene的原理和应用。深入了解前沿自然语言处理技术和底层索引性能调优的结合。 4. 学习亚马逊云上的各种云服务。 5. 参与产品需求讨论,提出技术实现方案。 6. 与国内外杰出的开发团队紧密合作,学习代码开发和审查的流程。 We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN