Theodore Vaslioudis, a former intern and full-time Amazon scientist
Theodore Vaslioudis, a former intern and full-time Amazon scientist since February 2020, uses his experiences to help customers gain the greatest value from AWS resources, and his colleagues make the most of working remotely.

From intern to applied scientist: How Theodore Vasiloudis made the transition

The applied scientist offers advice on how he utilized his internship to land a full-time job — and talks about how he and his colleagues won an award along the way.

In the early days of purchase data analysis, a study determined that people often bought diapers and beer together. When Theodore Vasiloudis, then a computer science undergrad, heard that from a professor at the Aristotle University of Thessaloniki, he was intrigued by the correlation: “I found it fascinating that, by aggregating the data of multiple users, you could extract weird and unexpected things like this.”

That course inspired Vasiloudis, today an applied scientist with Amazon Web Services (AWS), to direct his education toward machine learning. He left Greece in 2012 to study at the KTH Royal Institute of Technology in Stockholm, Sweden, which at the time had one of the few master’s programs in Europe dedicated to machine learning. After finishing his thesis on context-aware recommendations, he pursued an industrial PhD while employed at the Swedish Institute of Computer Science (industrial PhD students develop their research projects while working at a company to gain industrial experience).

In the final years of his PhD, Vasiloudis completed two summer internships at Amazon. One of those resulted in the publication of an award-winning research paper, Block-distributed Gradient Boosted Trees. In that paper, Vasiloudis and his colleagues Hyunsu Cho and Henrik Boström described the development of a new algorithm that was able to drastically reduce the communication cost to train massive, sparse datasets.

A full-time Amazon scientist since February 2020, Vasiloudis now uses his experiences to help customers make the best of AWS resources and his colleagues make the most of working remotely. He has even introduced to his team the custom of fika, the Swedish habit of pausing for a cup of coffee in the middle of the day. Each Friday, he and his teammates congregate over a remote coffee break at 3 p.m., which has helped sustain the team’s spirit during the pandemic. We asked Vasiloudis about his internship, what it was like to make the transition to full-time employee, and more.

Q. What made you interested in working at Amazon, and how was your experience as an intern?

With Amazon, you have the opportunity to reach hundreds of millions of people with your work. You can make changes that affect the everyday lives of such a large population. Also, because of the number of Amazon users, you are forced to design algorithms that can actually analyze massive amounts of data. So that's a very interesting challenge for me, to be able to create scalable algorithms that work regardless of the size of the data set.

For my first internship, I worked with Alexa Shopping and we looked into ways to generate realistic data sets to improve the customer’s experience. The second internship was with AWS, where my manager was Vineet Khare, then an applied science manager. There, I worked on how to get gradient-boosted trees to work with massive data sets that contain millions and billions of records, but also millions and billions of features. From that work, in close collaboration with my mentor Hyunsu Cho, we wrote the paper that won the best short paper award at SIGIR 2019.

These were both good experiences, because I got to work on interesting problems. And most importantly, I got to work with great colleagues. We had multiple interns within the team, and that meant that you could share the experience of being a science intern with other PhD students, and support each other through the internship. My full-time colleagues were also very helpful and fun to hang out with outside work as well. So I had a good time, and that's the main reason why I chose to return to Amazon for the full-time role.

One of the things that I definitely learned during my internships was the importance of writing high-quality code.  A common problem when you're writing research code is that you kind of go along without ensuring that everything works in a formal way. Whereas when writing code for a company, you need to prove and ensure that your code will always work regardless of the circumstances. And this is one of the Amazon leadership principles: That we have to insist on the highest standards.

Theodore Vasiloudis poses with the publication that won the best short paper award at SIGIR 2019.
Theodore Vasiloudis poses with the publication that won the best short paper award at SIGIR 2019.

Q. What set apart the paper that won at SIGIR 2019?

Gradient-boosted trees are designed to deal with very large data sets and are one of the most popular machine learning algorithms, widely used in both academia and industry. However, whenever we deal with very large data sets, often we have to use multiple computers.

Imagine you're trying to classify, for example, text. Let's say that this text is somebody’s loan application. If every possible word in this text is a feature, that means there can be millions of features because the vocabulary is practically limitless. So, when you try to share the model training among multiple computers — which can be a hundred, a thousand, or even more — you will very often run into problems because they are all competing for a tiny amount of bandwidth compared to the data set.

Previous systems were not efficient at communicating because they were wasting a lot of bandwidth with redundant information. Many real-world data sets are very sparse. In sparse data sets, most of the features are actually zeroes. Previous systems were still sending those over the network, and they were consuming a lot of unnecessary bandwidth. Whereas if you only send the non-zeroes over the network, then you're actually saving communication costs and bandwidth. That’s the main idea.

Q. How did you go about trying to find a solution for those sparse data sets?

We had two issues to solve: One regarding prediction and another regarding training. You can imagine a data set as a matrix. It has a bunch of rows, which are the records — for example, the loan application documents. And then each of those will have a number of features, which are the words in the document. So, you can have millions of documents, and millions of features as well. In previous systems, they would only partition the data set along the record dimension. They would take a few documents and put them in one computer, a few in another and then do the training and sync.

But if you want to really speed up the process, you can actually take part of a document and store it in one computer and another part in another computer. This is called block distribution. Instead of taking multiple rows from the same matrix, and storing them in the same computer, now we start taking a block — a few rows and a few columns — from that matrix and put it in one computer. That means that we have some additional communication to do to make predictions.

We used an existing algorithm for that called Quickscorer, which was designed for a completely different purpose, to speed up the prediction process locally. But that exact same approach can allow you to perform a very quick distributed prediction, and we modified that algorithm to adapt our use case. So that's how we solved that prediction issue. And then for the training, we did something similar, where we would only send for a given block the number of records that are necessary with a number of features, and then we would use an aggregation step in order to complete the training.

I think this work provides a good direction for future production systems. The communication pattern for very large data sets should be more flexible than the one that is currently used.

Q. What are you currently working on?

I'm working on SageMaker JumpStart. We create AWS solutions that allow customers to get started with SageMaker faster, and take their ideas to production more quickly and painlessly.  One part of my team’s responsibilities is to work directly with customers when they have a specific problem. But we also do a lot of innovating on the behalf of our customers.

Q. You started your full-time role right at the beginning of the pandemic. Did that affect your work in any way?

We stopped going to the office and started using a digital form of communication. In trying to keep the team spirit alive, one of the things that I try to have in our team is something that we used to have in Sweden, which is called the fika. It’s like a coffee break where you stop working for half an hour and chat with your colleagues about anything you want. It’s just some social time where everybody can relax and interact with colleagues.

If you have the opportunity to work at a company like Amazon, you should definitely take it, because you can gain a lot of experience that is impossible to gain during your PhD.
Theodore Vasiloudis

I saw that, with COVID-19, the interaction with colleagues goes down significantly, so it's good to have some time allocated in your calendar when you don’t have to work, just have some coffee and chat. An informal conversation is when a lot of important ideas come up, and it’s good to have that opportunity.

Q. What advice would you give to people considering following your footsteps?

If you have the opportunity to work at a company like Amazon, you should definitely take it, because you can gain a lot of experience that is impossible to gain during your PhD. The way that the industry works is very different from the way academia works. If you have done a couple of these internships, you're much more prepared to join the workforce.

For interns at Amazon hoping to migrate into a full-time job, I would say that the regular check-ins with your hiring manager are very important, because you need to be constantly aware whether you're on track for your full-time offer. Every second week you get to sit with your hiring manager, and you can check with them if you should be doing something more, if you're hitting your targets in terms of the progress of the work itself, and in terms of representing the leadership principles of Amazon in your work. And that gives you a better sense of accomplishment. You need to make sure that you set a few milestones in the meantime and make sure that you hit them as you progress through your internship.

Q. Any final tips on how to make the best out of your internship at Amazon?

How to become an intern at Amazon

If you’re a student with interest in an Amazon internship, you can find additional information here, and submit your details for review. Students can also learn more about internship opportunities at Amazon Student Programs.

Amazon values being independent and self-driven. And it's very good if you have a goal to publish a paper by the end of your internship and chase that publication. For example, we completed the writing of our paper after I had finished my internship, so if I hadn't pushed for that, I wouldn't have published this paper, and my co-authors and I wouldn't have gotten this award.

It's important to be motivated to work with your manager to make sure that you get all the necessary approvals before you finish your internship toward publishing the paper, because it's an important step for a career as a scientist, as well as for a PhD student, to publish high-quality papers. And it's a unique opportunity to do that when you have access to the infrastructure and data sets of Amazon.

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Job summaryAmazon Alexa is seeking an Applied Science Manager to drive initiatives on the leading edge of Machine Learning (ML), Natural Language Processing (NLP), Information Retrieval (IR), and Speech.Working collaboratively with scientists and engineers, you will design and implement automated, scalable NLP/ML/IR models for accessing and presenting information as well as improve products and features within Alexa. This exciting opportunity will impact the customer experience, design, architecture, and implementation of a cutting-edge product that will be used every day by people you know.If you are an entrepreneurial, data-driven, innovative, and influential individual who thrives on solving complex ambiguous problems and building innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you.Key job responsibilitiesIn this role you will,· Manage a team of high caliber Applied Scientists and Software Engineers working on building world class, scalable systems· Recruit, hire, mentor, and coach scientists and engineers at different levels of experience· Manage and execute against project plans and delivery commitments within an Agile/Scrum environment· Contribute to and lead design, architecture, process and development discussions· Own all operational metrics and support for your team’s software· Drive improvements in software engineering practices across engineering teams
AU, ACT, Canberra
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
AU, SA, ADELAIDE
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
CN, 11, Beijing
Job summaryAmazon 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.Your responsibilities include:· 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 goalsAmazon Search JP负责在Amazon JP购物网站上开发产品搜索功能,并将这些创新扩展到全球。作为这个快速发展中的团队的应用科学家,您将在改善NLP和Amazon产品搜索的体验方面发挥关键作用。我们的最终目标是帮助客户找到他们想要的产品,并发现他们感兴趣的新产品。我们通过开发涵盖多种语言和系统的NLP组件来达成目标。作为Search JP的应用科学家,您将在Amazon网站上设计和实现搜索功能,帮助数百万客户快速找到他们想要的内容。您将基于TB级的产品和流量数据提出NLP和IR领域的创新,构建机器学习模型,并使用离线指标以及A / B测试在线指标进行效果评估,然后将模型集成到面向客户的生产搜索引擎中,从而通过数据,建模,应用,和模型选择完成上线。您的模型同时需要平衡业务指标和毫秒级响应时间的要求。您的职责包括:•设计和实施新功能和机器学习模型,包括应用最先进的深度学习来解决搜索匹配,排名和搜索建议问题。•分析与搜索体验相关的数据和指标。•与全球团队合作开展全球项目。您的收获包括:•通过开发有高度影响力的产品改善数百万客户的体验•有机会使用和创造最新的机器学习方法来解决重要现实问题•作为成长中的团队的一员,我们将共同定义团队的使命,方向以及如何实现目标
IL, Tel Aviv
Job summaryAmazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, Amazon Echo and Halo.What will you help us create?Key job responsibilitiesYou will be part of a world-class Computer Vision team tasked with solving huge business problems through innovative technology and focus on product industrialization.We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company.Major responsibilities:· Research, design, implement and evaluate novel computer vision algorithms· Work on large-scale datasets, focusing on creating scalable and accurate computer vision systems in versatile application fields· Collaborate closely with team members on developing systems from prototyping to production level· Collaborate with teams spread all over the world· Work closely with software engineering teams to drive scalable, real-time implementations· Track general business activity and provide clear, compelling management reports on a regular basis
US, MN, Minneapolis
Job summaryMachine 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 Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab 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 and develop novel models 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. You will apply classical 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.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL 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 ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere 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 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 BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Bellevue
Job summaryAmazon AI is looking for world class scientists to join its AI Lab. This group is entrusted with developing core machine learning algorithms for AWS. As a part of the AI Lab you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore novel solutions to new problems at scale. 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.About UsInclusive Team CultureHere 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 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 BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, IL, Chicago
Job summaryMachine 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 Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab 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 and develop novel models 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. You will apply classical 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.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL 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 ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere 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 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 BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, TX, Austin
Job summaryMachine 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 Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab 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 and develop novel models 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. You will apply classical 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.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL 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 ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere 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 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 BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, MI, Detroit
Job summaryMachine 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 Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab 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 and develop novel models 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. You will apply classical 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.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL 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 ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere 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 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 BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.