Responsible AI in the wild: Lessons learned at AWS

Real-world deployment requires notions of fairness that are task relevant and responsive to the available data, recognition of unforeseen variation in the “last mile” of AI delivery, and collaboration with AI activists.

When we first joined AWS AI/ML as Amazon Scholars over three years ago, we had already been doing scientific research in the area now known as responsible AI for a while. We had authored a number of papers proposing mathematical definitions of fairness and machine learning (ML) training algorithms enforcing them, as well as methods for ensuring strong notions of privacy in trained models. We were well versed in adjacent subjects like explainability and robustness and were generally denizens of the emerging responsible-AI research community. We even wrote a general-audience book on these topics to try to explain their importance to a broader audience.

Related content
Generative AI raises new challenges in defining, measuring, and mitigating concerns about fairness, toxicity, and intellectual property, among other things. But work has started on the solutions.

So we were excited to come to AWS in 2020 to apply our expertise and methodologies to the ongoing responsible-AI efforts here — or at least, that was our mindset on arrival. But our journey has taken us somewhere quite different, somewhere more consequential and interesting than we expected. It’s not that the definitions and algorithms we knew from the research world aren’t relevant — they are — but rather that they are only one component of a complex AI workstream comprising data, models, services, enterprise customers, and end-users. It’s also a workstream in which AWS is uniquely situated due to its pioneering role in cloud computing generally and cloud AI services specifically.

Our time here has revealed to us some practical challenges of which we were previously unaware. These include diverse data modalities, “last mile” effects with customers and end-users, and the recent emergence of AI activism. Like many good interactions between industry and academia, what we’ve learned at AWS has altered our research agenda in healthy ways. In case it’s useful to anyone else trying to parse the burgeoning responsible-AI landscape (especially in the generative-AI era), we thought we’d detail some of our experiences here.

Modality matters

One of our first important practical lessons might be paraphrased as “modality matters”. By this we mean that the particular medium in which an AI service operates (such as visual images or spoken or written language) matters greatly in how we analyze and understand it from both performance and responsible-AI perspectives.

Consider specifically the desire for trained models be “fair”, or free of significant demographic bias. Much of the scientific literature on ML fairness assumes that the features used to compare performance across groups (which might include gender, race, age, and other attributes) are readily available, or can be accurately estimated, in both training and test datasets.

Related content
Two of the world’s leading experts on algorithmic bias look back at the events of the past year and reflect on what we’ve learned, what we’re still grappling with, and how far we have to go.

If this is indeed the case (as it might be for some spreadsheet-like “tabular” datasets recording things like medical or financial records, in which a person’s age and gender might be explicit columns), we can more easily test a trained model for bias. For instance, in a medical diagnosis application we might evaluate the model to make sure the error rates are approximately the same across genders. If these rates aren’t close enough, we can augment our data or retrain the model in various ways until the evaluation is passed to satisfaction.

But many cloud AI/ML services operate on data that simply does not contain explicit demographic information. Rather, these services live in entirely different modalities such as speech, natural language, and vision. Applications such as our speech recognition and transcription services take as input time series of frequencies that capture spoken utterances. Consequently, there are not direct annotations in the data of things like gender, race, or age.

But what can be more readily detected from speech data, and are also more directly related to performance, are regional dialects and accents — of which there are dozens in North American English alone. English-language speech can also feature non-native accents, influenced more by the first languages of the speakers than by the regions in which they currently live. This presents an even more diverse landscape, given the large number of first languages and the international mobility of speakers. And while spoken accents may be weakly correlated or associated with one or more ancestry groups, they are usually uninformative on things like age and gender (speakers with a Philadelphia accent may be young or old; male, female or nonbinary; etc.). Finally, the speech of even a particular person may exhibit many other sources of variation, such as situational stress and fatigue.

Regional dialects.jpeg
Data — such as regional variations in word choice and accents — may lead toward alternative notions of fairness that are more task-relevant, as with word error rates across dialects and accents.

What is the responsible-AI practitioner to do when confronted with so many different accents and other moving parts, in a task as complex as speech transcription? At AWS, our answer is to meet the task and data on their own terms, which in this case involves some heavy lifting: meticulously gathering samples from large populations of representative speakers with different accents and carefully transcribing each word. The “representative” is important here: while it might be more expedient to (for instance) gather this data from professional actors trained in diction, such data would not be typical of spoken language in the wild.

Related content
Both secure multiparty computation and differential privacy protect the privacy of data used in computation, but each has advantages in different contexts.

We also gather speech data that exhibits variability along other important dimensions, including the acoustic conditions during recording (varying amounts and types of background noise, recordings made via different mobile-phone handsets, whose microphones may vary in quality, etc.). The sheer number of combinations makes obtaining sufficient coverage challenging. (In some domains such as computer vision, coverage issues that are similar — variability across visual properties such as skin tone, lighting conditions, indoor vs. outdoor settings, and so on — have led to increased interest in synthetic data to augment human-generated data, including for fairness testing here at AWS.)

Once curated, such datasets can be used for training a transcription model that is not only good overall but also roughly equally performant across accents. And “performant” here means something more complex than in a simple prediction task; speech recognition typically uses a measure like the word error rate. On top of all the curation and annotations above, we also annotate some data by self-reported speaker demographics to make sure we’re fair not just by accent but by race and gender as well, as detailed in the service’s accompanying service card.

Our overarching point here is twofold. First, while as a society we tend to focus on dimensions such as race and gender when speaking about and assessing fairness, sometimes the data simply doesn’t permit such assessments, and it may not be a good idea to impute such dimensions to the data (for instance, by trying to infer race from speech signals). And second, in such cases the data may lead us toward alternative notions of fairness that might be more task-relevant, as with word error rates across dialects and accents.

The last mile of responsible AI

The specific properties of individuals that can or cannot (or should not) be gleaned from a particular dataset or modality are not the only things that may be out of the direct control of AI developers — especially in the era of cloud computing. As we have seen above, it’s challenging work to get coverage of everything you can anticipate. It’s even harder to anticipate everything.

The supply chain phrase “the last mile” refers to the fact that “upstream” providers of goods and products may have limited control over the “downstream” suppliers that directly connect to end-users or consumers. The emergence of cloud providers like AWS has created an AI service supply chain with its own last-mile challenges.

Related content
The team’s latest research on privacy-preserving machine learning, federated learning, and bias mitigation.

AWS AI/ML provides enterprise customers with API access to services like speech transcription because many want to integrate such services into their own workflows but don’t have the resources, expertise, or interest to build them from scratch. These enterprise customers sit between the general-purpose services of a cloud provider like AWS and the final end-users of the technology. For example, a health care system might want to provide cloud speech transcription services optimized for medical vocabulary to allow doctors to take verbal notes during their patient rounds.

As diligent as we are at AWS at battle-testing our services and underlying models for state-of-the-art performance, fairness, and other responsible-AI dimensions, it is obviously impossible to anticipate all possible downstream use cases and conditions. Continuing our health care example, perhaps there is a floor of a particular hospital that has new and specialized imaging equipment that emits background noise at a specific regularity and acoustic frequency. In the likely event that these exact conditions were not represented in either the training or test data, it’s possible that overall word error rates will not only be higher but may be so differentially across accents and dialects.

Such last-mile effects can be as diverse as the enterprise customers themselves. With time and awareness of such conditions, we can use targeted training data and customer-side testing to improve downstream performance. But due to the proliferation of new use cases, it is an ever-evolving process, not one that is ever “finished”.

AI activism: from bugs to bias

It’s not only cloud customers whose last miles may present conditions that differ from those during training and testing. We live in a (healthy) era of what might be called AI activism, in which not only enterprises but individual citizens — including scientists, journalists, and members of nonprofit organizations — can obtain API or open-source access to ML services and models and perform their own evaluations on their own curated datasets. Such tests are often done to highlight weaknesses of the technology, including shortfalls in overall performance and fairness but also potential security and privacy vulnerabilities. As such, they are typically performed without the AI developer’s knowledge and may be first publicized in both research and mainstream media outlets. Indeed, we have been on the receiving end of such critical publicity in the past.

Related content
Technique that mixes public and private training data can meet differential-privacy criteria while cutting error increase by 60%-70%.

To date, the dynamic between AI developers and activists has been somewhat adversarial: activists design and conduct a private experimental evaluation of a deployed AI model and report their findings in open forums, and developers are left to evaluate the claims and make any needed improvements to their technology. It is a dynamic that is somewhat reminiscent of the historical tensions between more traditional software and security developers and the ethical and unethical hacker communities, in which external parties probe software, operating systems, and other platforms for vulnerabilities and either expose them for the public good or exploit them privately for profit.

Over time the software community has developed mechanisms to alter these dynamics to be more productive than adversarial, in particular in the form of bug bounty programs. These are formal events or competitions in which software developers invite the hacker community to deliberately find vulnerabilities in their technology and offer financial or other rewards for reporting and describing them to the developers.

Bias bounties.png
In a fair-ML (“bias bounty”) competition, different teams (x-axis) focus on different demographic features (y-axis) in the dataset, indicating that crowdsourced bias mitigation can help contend with the breadth of possible sources of bias. (The darker the blue, the greater the use of the feature.)

In the last couple of years, the ideas and motivations behind bug bounties have been adopted and adapted by the AI development community, in the form of “bias bounties”. Rather than finding bugs in traditional software, participants are invited to help identify demographic or other biases in trained ML models and systems. Early versions of this idea were informal hackathons of short duration focused on finding subsets of a dataset on which a model underperformed. But more recent proposals incubated at AWS and elsewhere include variants that are more formal and algorithmic in nature. The explosion of models, interest in, and concerns about generative AI have also led to more codified and institutionalized responsible-AI methodologies such as the HELM framework for evaluating large language models.

We view these recent developments — AI developers opening up their technology and its evaluation to a wider community of stakeholders than just enterprise customers, and those stakeholders playing an active role in identifying necessary improvements in both technical and nontechnical ways — as healthy and organic, a natural outcome of the complex and evolving AI industry. Indeed, such collaborations are in keeping with our recent White House commitments to external testing and model red-teaming.

Responsible AI is neither a problem to be “solved” once and for all, nor a problem that can be isolated to a single location in the pipeline stretching from developers to their customers to end-users and society at large. Developers are certainly the first line where best practices must be established and implemented and responsible-AI principles defended. But the keys to the long-term success of the AI industry lie in community, communication, and cooperation among all those affected by it.

Related content

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!
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.
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, 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
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, 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, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
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.
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!