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.

Related content

US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
US, WA, Seattle
Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We are looking for a Research Scientist to join our growing Sustainability team to drive the science behind value chain decarbonization. This role will establish Amazon's scientific methodologies for sector- and cross-sectoral decarbonization mechanisms, and establish benchmarks for automated validation and risk assessment. As a Research Scientist, you will be responsible for independently leading assessments of environmental issues across the full spectrum of Amazon businesses and evaluating sustainability impacts across the value chain. You will independently develop quality frameworks and methodologies that enable Amazon to scale procurement of high-quality environmental interventions while maintaining scientific rigor and environmental integrity. Key job responsibilities Develop quality assessment frameworks for complex environmental interventions, baseline-setting approaches, and measurement methodologies Build quantitative benchmark and statistical models that enable scalable evaluation across heterogeneous data sources Create attribution methodologies for supply chain interventions across Amazon's diverse footprint Develop social and environmental safeguard criteria that integrate community impact assessments Collaborate with cross-functional teams including procurement, sustainability operations, and business units to translate scientific methodologies into operational requirements Work under the direction of senior business leaders while acting as lead Subject Matter Expert for value chain decarbonization science, including designing and leading research, data collection, modeling, documentation, interpretation, and validation About the team Diverse Experiences: World Wide Sustainability 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. Inclusive Team Culture: 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 (inclusive 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.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! The Prime Video Title Lifecycle Presentation team sits at the intersection of science, experimentation, and customer experience. We leverage data signals and rigorous testing to present the most engaging information about our content to customers at precisely the right moment. Our mission is to ensure every customer interaction with Prime Video content is informed, relevant, and compelling in order to drive discovery and engagement across our vast catalog. We're seeking a Sr. Applied Scientist who excels at building sophisticated machine learning systems for content presentation and discovery. The ideal candidate brings deep expertise in: - Multi-modal embeddings for rich metadata representation, enabling nuanced understanding of content attributes and customer preferences - Contextualized ranking systems that adapt to customer intent, viewing context, and real-time signals - Reinforcement learning frameworks that create continuous improvement loops, allowing our systems to learn and optimize from customer interactions over time - General modeling techniques with strong fundamentals in machine learning and statistical methods - Recommender systems experience, with proven ability to build and scale personalization solutions You'll work with technology to solve complex problems in content discovery, leveraging large-scale data to create experiences that delight millions of Prime Video customers worldwide. Key job responsibilities - Lead Cross-Functional Science Initiatives: Drive a diverse portfolio of applied science projects spanning recommender systems, generative AI agent development and evaluation across multiple modalities, and computer vision applications. Demonstrate both breadth of understanding across technical domains and sufficient depth in each area to effectively lead multiple concurrent initiatives to successful outcomes. - Bridge Science and Engineering for Production-Scale Deployment: Partner with engineering teams to productionize machine learning models at Prime Video scale. Develop production-ready science code that meets engineering standards for performance, reliability, and maintainability, ensuring seamless transition from research to deployment. - Mentor and Develop Technical Talent: Provide technical mentorship and guidance to junior scientists and engineers on applied science methodologies, best practices, and professional development. Foster a culture of scientific rigor and continuous learning within the team.
IN, KA, Bengaluru
Alexa+ is the world’s best Generative AI powered personal assistant / agent for consumers, and is becoming the conversational AI interface for Amazon services with the launch of Alexa for Shopping on Amazon.com and Amazon mobile app. At Alexa Ads, we are creating industry's first and most advanced Agentic Advertising products to drive Agentic Commerce. We are seeking an Applied Scientist to join our newly expanding team in India focused on Alexa Agentic/Conversational Ads and Personalization. In this role, you will build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions. You will work closely with other scientists, engineers, and product managers to take models from conception to production. Key job responsibilities - Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization. - Conduct hands-on data analysis and build scalable ML pipelines. - Design and run A/B experiments to measure the impact of new models on customer experience and ad performance. - Collaborate with software development engineers to deploy models into high-scale, real-time production environments. About the team We are building a new science team in Bangalore to solve some of the most impactful problems in computational advertising. This isn't about tweaking existing models as we are rethinking how ads are ranked, priced, and personalized across voice-first and screen-first surfaces. These are problems that don't have textbook solutions. Key points to note about the team: 🧪 Greenfield team - you are not joining a mature org with rigid processes. You will shape the science roadmap, pick the problems, and define the culture from day one. 📈 Direct business impact — your models directly drive revenue. No yearly cycles to see if your work matters. 🌏 Global scope, local autonomy — collaborate with scientists and engineers across Seattle, Sunnyvale, and Bangalore, but own your problem space end-to-end. 🎓 Ship AND Publish: We encourage top-tier publications (NeurIPS, ACL, EMNLP, KDD, ICML, WWW) while ensuring your research hits production.
US, CA, Pasadena
The Amazon Center for Quantum Computing (CQC) in Pasadena, CA, is looking to hire an Applied Scientist in the Quantum Error Correction Theory Team. You will be joining a multi-disciplinary team of scientists working at the forefront of quantum computing. The ideal candidate should posses comprehensive knowledge of quantum fault tolerance theory, including: - Topological, concatenated and low density parity check codes - Quantum decoding algorithms - Fault-tolerant logical protocols You will contribute to developing hardware-aware fault-tolerant protocols and software tools for performance analysis under realistic noise models, helping guide our organization toward delivering a fault-tolerant quantum computer. Key job responsibilities - Design and evaluate novel quantum error correction schemes, including new codes and fault-tolerant protocols - Research and develop hardware-optimized architectures for fault-tolerant computing - Drive experimental implementations of quantum fault-tolerance - Develop simulation and modeling software tools - Lead high-impact, collaborative research projects aligned with our engineering roadmap - Apply strong engineering principles while maintaining scientific rigor A day in the life Based in Pasadena, CA, you'll work in our purpose-built facility, designed for quantum computing research and development, adjacent to Caltech's campus. As an applied scientist in the quantum error correction team, you will: - Drive independent research while collaborating on fault tolerance projects within our architecture - Stay abreadth with current developments in the active field of fault-tolerance through joint Caltech seminars and conference participation - Partner with experimental scientists to enhance logical performance of quantum processors in a unique opportunity to bridge theory and experiment - Design error correction schemes optimized for specific quantum algorithms - Collaborate with control stack engineers to enable increasingly sophisticated fault-tolerance experiments - Work with systems engineers to identify, understand, and model technical requirements - Contribute to regular team discussions and joint planning sessions About the team Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a U.S export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. 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. 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. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Standardized AWS Taglines About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. 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. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Key job responsibilities Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, CA, Palo Alto
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Sr Data Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As a Data Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
US, TX, Austin
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3GPP-compliant service to unmodified commercial handsets. The Role: Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Research, design and specify RRM algorithms for Amazon Leo’s 3GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services. • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO). • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum. • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity. • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation. • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior. • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures. • Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment. • Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.
US, CA, San Diego
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. Come work at Amazon! The Role: Be part of the team defining the overall communication system and architecture of Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in integration, verification and testing of the systems especially focused on MAC and higher layer testing. This role will also be responsible developing and testing advanced L1/L2/L3 concept to improve the performance and reliability of the LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. In this role you will: - Work within a project team and take the responsibility for the Leo’s communication system design, system integration and verification. - Work as a part of the team in building a suite of system and network simulation services in Matlab / C++ / Python - Develop requirements from system level to HW/SW level and define test cases associated with the requirements. - Identify additional HW and SW that are needed for the purposes of verification and guide the HW/SW development team in the development of these test solutions/tools// - Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements - Write scripts / code for functions / features required for specific simulation, testing and verification of given RF system EXPORT CONTROL REQUIREMENTS Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
US, WA, Seattle
Amazon Economics is seeking Structural IO Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available STRUC internships in 2027. Key job responsibilities As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: - Analyze large-scale datasets using structural econometric techniques to solve complex business challenges - Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions - Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization - Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value - Build and refine comprehensive datasets for in-depth structural economic analysis - Present complex analytical findings to business leaders and stakeholders