PhD students from Amazon's first class of remote interns: Alesia Chernikova, Meghana Palukuri, Zihao Wang, Kai Xiao, Lisa Yu
From left to right: Alesia Chernikova, Meghana Palukuri, Zihao Wang, Kai Xiao, and Lisa Yu. These five PhD students were among Amazon's first class of remote interns.
Glynis Condon

2020 science interns discuss what it’s like to intern virtually

Learn how these five PhD students used technology to stay connected, and get the most out of a unique internship experience.

This year, Amazon hosted more than 8,000 interns across the globe. That figure is significant for two reasons: First, it’s the largest class of interns in company history. Second, for the vast majority of interns this year, their entire internship was virtual. We asked five interns what it was like to intern remotely, and to share how that shaped their experiences.

Below they talk about what it’s like to be an intern during a global pandemic, the vital role of technology in ensuring their experiences were enriching, and the advice they would give to future interns.

As a science intern, what excites you about the future of your field after your internship? What is the most valuable experience or learning you take away from your internship? 

Zihao Wang, Emory University, PhD student in computer science and applied scientist intern: The application of deep learning on spoken language understanding will continue to have a critical impact on improving satisfaction of users in conversations with conversational agents. This is important because conversational agents are used in many fields right now and will be used in more and more fields to affect people’s everyday lives. The most valuable experience is that in the real life applications, we not only need to work on common problems, but also on long-tail problems, and in many cases, it’s the long-tail problem that will impact tremendously on user experience.

Meghana Palukuri, The University of Texas at Austin, PhD student in computational science, engineering and mathematics and applied scientist intern: My internship was the first opportunity I had to dive deeper into the field of natural language processing. The impact that can be created by the field is inspiring, as a lot of data is available in the textual form and analyzing it can yield powerful and useful algorithms. For instance, in my internship project, I built a product embedding space for making substitute product recommendations. I am excited about how the performance of models like these can be improved by advances in the field with better text representations (sentence embeddings). The most valuable learning from my internship is to never stop learning, for example, by spending time reading state-of-the-art research papers. 

Alesia Chernikova, Northeastern University, PhD student in computer science and applied scientist intern: During the internship, I was excited to work in the field of scientific research, which currently is in great demand in both academia and industry, and which has the prerequisites for further improvement and development. While working on the project, I was happy to have the opportunity to develop a new methodology to solve the existing problem, and put it into practice on real data. Last but not least, I learned how to dive deeply into the problem and look at it from different perspectives, taking into account the already existing solutions.

What was the single most useful tool you used during your virtual internship?

Lisa Yu, The George Washington University, PhD student in statistics and data science intern: For my home-office set-up, the 32-inch 4K monitor provided by Amazon was extremely useful! I enjoy it almost too much.

Kai Xiao, North Carolina State University, PhD student in computer science and applied scientist intern: I would say definitely the wide monitor. I could put so much stuff on it yet was still able to effectively find what I needed. This is crucial to tech roles, especially when you have to keep multiple terminals open for reading code, which would normally require scrolling up and down with a regular sized monitor. One other thing is subscribing to the research mailing lists. When I had technical questions that I couldn’t find answers for, sending questions to the broader mailing list always helped. There’s always someone subscribed to the list that can help with the aspect you’re not familiar with and it also served as a  good resource to find mentors.

Alesia Chernikova: We used a whiteboard application that was very helpful when I discussed the theoretical part of my project, such as mathematical formulas, algorithms, and coding techniques. We also used it during our research group meetings when someone presented new information to other people in the team. In addition, the Chime calling feature was really helpful. Whenever I needed help or clarification from my colleagues, I could easily call or message them to get the answer to my question.

Meghana Palukuri: As an intern who had not met anyone on my team before joining, technology that connects people was essential for work,  for developing interpersonal relationships ,and for providing support during these times, when a lot of us are isolated at our homes. Sometimes, we would be on a call together while working, just to give us the feeling of being in the same office space.

Zihao Wang: Although COVID-19 prevented us from meeting in person, the technology enabled us to interact with each other pretty closely. Chime, Slack, Quip, and other online docs, as a united suite of technology tools, helped me get onboarded, acquire essential knowledge, communicate with teammates, network, and make progress. Through these tools, I felt very warmly welcomed, and strongly supported by my teammates.

Which events did you find the most helpful?

Kai Xiao: The amount of exposure to new knowledge was a big bonus interning at Amazon. I constantly looked for things in our internal posters site (since we didn’t have access to company elevators), and most of the experiences were positive and engaging. Much of my fulfillment came from finally having a place to utilize my communication skills within these events. Being virtual means you lose most of the interaction, and having these events really went a long way in maintaining a certain level of engagement between me and the company. I was always excited when finding some new events to attend.

Meghana Palukuri: The Science Intern Cohort Program gave me the opportunity to connect with fellow PhD students working on very interesting research problems. Putting together a poster for the Graduate Research Poster session felt great, and also helped me prepare for my final presentation. The Alexa Skills Hackathon was also really fun. I worked with four other interns to build my first Alexa skill – ‘intern chat’ to enhance the pre-onboarding experience for future interns. I took part in the three-day MLA-NLP (machine learning accelerator – natural language processing) workshop, attended by both interns and full-time employees. The final project, on classification of customer product reviews, was a great learning experience.

Alesia Chernikova: I attended the Science Cohort Program, speaker series/intern panel, and poster session. All of these student events helped me to learn more about Amazon, the variety of teams and projects, and internship-related questions. All these activities were especially helpful in virtual environment settings. For instance, by virtue of the Science Cohort Session, I built connections with other interns, listened to their experiences during the internship, and learned about the research projects they were working on both at university and Amazon. It was beneficial for me to look at things not only from the inside, but also understand it from the perspective of others.

What is one piece of advice you would give to future interns?

How to apply

Amazon’s Graduate Research internship program includes mentorship, moderated discussion groups, opportunities to connect with fellow interns, fireside chats with senior leaders, and a variety of networking events.

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.

Lisa Yu: For future interns, if they are doing virtual internships, the most important thing I want to mention is communication! I suggest participating in team hang-outs to interact with and become familiar with their team members. Also, attend intern events to communicate with other interns in order not to feel alone during a virtual internship.

Kai Xiao: Communication is the key – especially with stakeholders. These people are normally your direct reports, mentors, and team members, but most importantly your customers! A good way to avoid anxiety is to plan your day, especially in a virtual setup. Every morning I would pull out my calendar, “time box” certain blocks of the day to be work time. It is perfectly OK if some of the time boxes are leisure times, as long as that’s in your plan. This method saved my virtual internship this year.

Zihao Wang: I would say to future interns that it’s never too early to start preparing yourself for new internship/job opportunities. Start acquiring  as much knowledge as you can that’s relevant to your research interest, such as domain knowledge, usage of tools, and even communication skills. You will find yourself in situations where you feel you should have honed your skills before, or where you actually feel lucky that you acquired that skill during  your spare time.

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Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. Key job responsibilities • Use machine learning and analytical techniques to create 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, development, evaluate and deploy innovative and highly scalable models for predictive learning • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. Key job responsibilities Use machine learning and analytical techniques to create 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, development, evaluate and deploy innovative and highly scalable ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems. About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.