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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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.