senior applied science manager Ali Dashti stands outside with a cityscape in the background
Amazon's Internet Famous page is the brainchild of senior applied science manager Ali Dashti's Discovery Tech team. His team uses machine learning to help Amazon Store customers find new products.

How Ali Dashti helps advance the science behind marketing collections

The senior applied science manager envisions machine learning as the path to a better experience for Amazon customers.

Social media can have a big influence on the popularity of certain items. Take, for example, the LEGO Flower Bouquet Building Kit featured on the show Abbott Elementary or the "miracle cleaning paste" seen in millions of online videos. Both have picked up buzz from viral clips and sharing.

On Amazon's Internet Famous page, you can find these and many other products people are talking about — without all the video clips and scrolling. The collection is the brainchild of Ali Dashti's Discovery Tech team, which helps connect shoppers at the Amazon Store with the new and exciting products.

Dashti leads a team at Amazon that collaborates with scientists across multiple organizations to steer the research behind behind building Amazon Store collections, driving recommendations, and improving personalization for customers. He joined Amazon in 2019 after several years in academia — a transition that has been marked by pleasant surprises.

"When I joined Amazon, I was thinking of myself as a small cog in this big machine, but that's not really the case," Dashti says. "You can really have an impact here, in the sense that you can drive business decisions and customer satisfaction."

Exploring new ways to shop on Amazon

Unsurprisingly, many people interact with the Amazon Store through search. You arrive with an idea of what you are looking for, type in your query, and browse the results. While effective, this is just one way to shop. Dashti's team is looking at other ways customers might discover their next favorite thing in the Amazon store.

"Is it possible to digest this list of hundreds of millions of products into smaller collections — thousands of products in tens of categories — that are connected on a narrative, such as specific events like Mother’s Day or back to school?” he elaborates. “Then we want to personalize them for our customers to discover based on their taste and shopping intent."

Related content
The story of a decade-plus long journey toward a unified forecasting model.

He breaks this challenge down into two aspects. One is collections built around events and seasonality. The Discovery Tech science team trained a machine learning model that uses seasonality forecasts, recurring marketing input, and collective customers’ past behavior to create collections such as fall or spring favorites and back to school. Another example is evergreen collections such as Internet Famous, which detects cool and viral products featured by influencers year-round. The model uses those signals to automatically create landing pages which feature those products and are discoverable by customers.

The idea for the Internet Famous feature came from a question that came up on the team: Could an algorithm identify whether an image is “cool,” based on buzz from social media influencers? The resulting feature links Amazon’s inventory with conversations on social media platforms.

Our work is more about how we can really understand what people want based on what we know about their short-term and long-term preferences and give our customers the serendipitous sense of discovery in their shopping journey.
Ali Dashti

“We trained a deep learning model on data from influencers to be a 'cool detector' for the Amazon catalog,” he says.

The second part of the personalization problem, Dashti says, is what the team calls automated merchandising: connecting the right products to individual customers.

“Now that we have these collections, how do we drive traffic to them? If a customer is looking at a product, maybe we can recommend some other products that are internet famous or spring favorites, based on what that customer is viewing,” he explains.

He added that the team is thinking about how to drive discovery for these collections in places where there is no specific intent by customers. For example, the Amazon homepage or an email might offer a “discover customers’ most-loved for you” grouping.

Automated merchandising involves the scientific challenge of making an AI-based personal product recommender of sorts for Amazon customers, answering the question of what content, where in the customer journey, and at what time. It goes beyond creating a set of rules where you might, say, display more shoes if someone has searched for shoes.

Related content
Ren Zhang and her team tackle the interesting science challenges behind surfacing the most relevant offerings.

“Our work is more about how we can really understand what people want based on what we know about their short-term and long-term preferences and give our customers the serendipitous sense of discovery in their shopping journey, even if they are not looking for a specific category of products,” he says. “Another tenet of our personalization charter is how can we make our recommendations explainable.”

Dashti refers to an explosion of innovation in AI over the past few years based on large language models that can generate text much as a human would.

“This is what we can leverage to improve how our customers experience events such as Father’s Day and back to school — understanding customer journeys as a sequence of preferences and behaviors such as shopping intents, page visits, et cetera, to leverage existing transformer-based language models that help customers sort through the huge catalog of products we have at Amazon and ensure they have a bar-raising experience,” he says.

A pivot from university to tech

Dashti’s academic focus at the University of Wisconsin Milwaukee, cryo-electron microscopy, was seemingly a far cry from what he is doing now. But there is a common thread: He was writing algorithms designed to uncover insights buried in data. When Dashti was an undergraduate at Sharif University of Technology in Iran, a professor and mentor introduced him to the research area of brain-computer interfaces.

During his fourth year, he wrote an algorithm that could identify tasks like thinking about writing a poem or rotating an object based on electroencephalogram signals. From that project, he says, “I got hooked.” He knew he wanted to pursue some form of machine learning.

Related content
How her background helps her manage a team charged with assisting internal partners to answer questions about the economic impacts of their decisions.

At the University of Wisconsin, where he earned a master’s in electrical and electronics engineering and a doctorate in biomedical and healthcare informatics, he became interested in cryo-electron microscopy, which can produce atomic-level images of frozen biological samples. He built an algorithm that could help identify conformational changes of molecular machines during their work cycle based on geometric data. His work was cited in the scientific significance section of the 2017 Nobel Prize in chemistry, which cited the development of the imaging technique and its ability to generate 3D images of biomolecules.

After several years, he had built a prestigious academic career and was living comfortably in Milwaukee with his wife and two children. But he had thoughts of moving to industry, where his work would have more tangible impacts. When a recruiter from Amazon reached out, he responded, and before long he was moving to Seattle to join the Fashion Marketing team as an applied scientist.

Soon after he joined Amazon, Carmen Nestares, who was then the group’s chief marketing officer, invited Dashti to get coffee and talked to him about the company’s Day One culture, encouraging him to make his mark.

“This was my boss’s boss’s boss. It was completely out of the blue,” he says. “She really gave me this confidence and ownership that I needed at the time.”

In his first year at the company, Dashti wrote a brief about attribution, the process of determining how different marketing campaigns link to a given purchase. He thought maybe a couple of people would read it.

To his surprise, the brief sparked change. “It went into the roadmap for the next year. A year after that, the team had incorporated my findings into how they thought about attribution. That was amazing,” he said.

Related content
Dual embeddings of each node, as both source and target, and a novel loss function enable 30% to 160% improvements over predecessors.

Dashti later joined Nestares in building Discovery Tech, where he now manages a team of scientists. He describes Amazon as being like a group of 10,000 startups. “You can have all the freedom of a startup, all that learning experience of putting on multiple hats,” he says. “But you have all the wealth of knowledge in the whole field at your disposal.”

The culture lends itself to a balance between immediate projects and what he has called long-term science discovery moonshots. Among other projects, the team is collaborating with Amazon Scholars Yury Polyanskiy and Sasha Rakhlin, professors of computer science at MIT, in a moonshot-level effort to map customer interactions with products onto complex graph networks to enhance personalization. Another moonshot would be to turn advances in text-to-image generation and computer vision toward searching Amazon’s catalog in new ways — by generating an image based on your own words and surfacing matching products, for example.

In addition to the collaborative nature of his work with the Discovery Tech team, Dashti has appreciated the chance to work with a diverse team and to grow in ways that go beyond technical experience. Parity for women is particularly important to him, given the recent protests in Iran, and he appreciates having mostly women leaders on his current team at Amazon.

“I have always been surrounded by powerful women,” he says, mentioning his mother and his wife, who also grew up in Iran. “Having more women in higher management in tech is a must. It brings balance, pragmatism, empathy — qualities that are really driving this organization.”

As a manager, Dashti supports scientists on his team, about a third of which are women, in pursuing their big ideas. He remembers times in his career before Amazon, he says, when he didn’t really like what he was doing, and it was just a job. He strives to make sure no one on his team reaches that point.

“It starts with ownership,” he says. “I give team members the power to choose what they want, but also the responsibility of seeing the impact of what they do. It’s a management style that requires a lot of trust.”

Related content

US, WA, Seattle
Alexa is the Amazon cloud service that powers Echo, the groundbreaking Amazon device designed around your voice. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. Alexa Audio is responsible for fulfilling customers requests for all types of audio content (Music, Radio, Podcasts, Books, custom sounds) across all Alexa enabled devices. This covers a broad set of experiences including search, browse, recommendations, playback, and devices grouping and controls. We are seeking a talented, self-directed Applied Scientists who would come up with state of the art semantic search and recommendation techniques that work with both voice and visual interfaces. This is a unique opportunity where you will be working on latest technologies including LLMs, and also see it impact customer's lives in meaningful ways. Responsibilities - Apply advance state-of-the-art artificial intelligence techniques and develop algorithms in areas of personalization, voice based dialogue systems and natural language information retrieval. - Design scientifically sound online experiments and offline simulations to study and improve products. - Work closely with talented engineers to create scalable models and put them to production. - Perform statistical analyses on large data sets, identify problems, and propose solutions. - Work with partner science teams to identify collaboration opportunities. Work hard. Have fun. Make history. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
ES, M, Madrid
Amazon's International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site. The team, mainly located in Madrid Technical Hub, London and Luxembourg, comprises Software Developer and ML Engineers, Applied Scientists, Product Managers, Technical Product Managers and UX Designers who are experts on several areas of ranking, computer vision, recommendations systems, Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW? Are interesting on how we use state of the art AI to generate and provide the most relevant content? Key job responsibilities We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team, including working with text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design, develop, deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables. We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
GB, Cambridge
The Amazon Artificial General Intelligence (AGI) team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for building large-scale, high-quality conversational assistant systems. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information representation, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, cpu, latency and quality - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing and verification - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team A day in the life As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
DE, Berlin
The Amazon Artificial General Intelligence (AGI) team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for building large-scale, high-quality conversational assistant systems. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information representation, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, cpu, latency and quality - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing and verification - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team A day in the life As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU
US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services . We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS Measurement, targeting, recommendation, forecasting related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with economists, scientists and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing measurement, valuation and machine learning capabilities directly leading to improvements in our key performance metrics. A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning models and causal inference. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers. 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 * Lead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization. * Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches. * Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches. * Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines. * Publish and present your work at internal and external scientific venues in the fields of ML and causal inference. * Influence long-term science initiatives and mentor other scientists across AWS. A day in the life 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | New York City, NY, USA | Seattle, WA, USA
US, CA, Santa Clara
Amazon is looking for world class scientists and engineers to join its AWS AI Labs working within natural language processing. This group is entrusted with developing core data mining, natural language processing, and machine learning solutions for AWS services. At AWS AI Labs you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually large scale natural language processing solutions. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. 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. About the team 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. A Senior Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be working with a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll participate the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Seattle
Are you a scientist interested in pushing the state of the art in Generative AI, LLMs, LMMs? Are you interested in working on ground-breaking research projects that will lead to great products and scientific publications? Do you wish you had access to large datasets? Answer yes to any of these questions and you’ll fit right in here at Amazon. We are looking for a hands-on researcher, who wants to derive, implement, and test the next generation of Generative AI algorithms in multiple projects ranging from Computer Vision, ML, and NLP. The research we do is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish cutting edge research. In order to achieve our vision, we think big and tackle technology problems that are cutting edge. Where technology does not exist, we will build it. Where it exists we will need to modify it to make it work at Amazon scale. We need members who are passionate and willing to learn. Key job responsibilities - Derive novel computer vision, machine learning, and NLP algorithms. - Define scalable computer vision, machine learning and NLP models. - Invent the next generation of Generative AI models. - Work with large datasets. - Work with software engineering teams to deploy your - Publish your work at top conferences/journals. - Mentor team members. A day in the life We are a team of seasoned scientists. We work on science problems and publish our results at major scientific conferences. We work with multiple other science teams at Amazon. About the team We are a tight-knit group that shares our experiences and help each other succeed. We believe in team work. We love hard problems and like to move fast in a growing and changing environment. We use data to guide our decisions and we always push the technology and process boundaries of what is feasible on behalf of our customers. If that sounds like an environment you like, join us. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA