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
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities - Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment - Develop and deploy models spanning computer vision, language, and search and retrieval that operate on multimodal inputs at scale - Design and analyze large-scale online experiments to measure impact on shopper and advertiser outcomes - Collaborate with engineering, product, and design to ship science into production A day in the life As an Applied Scientist on the Sponsored Videos team, you will tackle problems at the intersection of computer vision, generative AI, search and retrieval, and personalization. You'll own the full science lifecycle from research and experimentation through online testing and production deployment, working closely with engineering, product, and design partners to bring ideas to market. You should be comfortable working with multimodal signals, building models that operate at scale, and measuring impact through rigorous experimentation. Your work will directly influence the experience of hundreds of millions of shoppers and the outcomes of tens of thousands of advertisers. About the team The Sponsored Videos team within Sponsored Products and Brands develops the science and systems behind video advertising experiences that connect advertisers and shoppers across Amazon. We are on a mission to make Amazon the best-in-class destination for shoppers to discover, engage with, and build affinity with brands through videos.
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.
CA, BC, Vancouver
Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure. We are looking for a Sr. Applied Scientist to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Software Engineers, Machine Learning Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities * Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams * Priority management - manage diverse requests and dependencies from teams * Process improvements – define, implement and continuously improve delivery and operational efficiency * Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations * Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better.
US, CA, Sunnyvale
Amazon 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 industry leading devices like Fire tablets, Fire TV and Amazon Echo. As a Design Analysis Engineer, you will be responsible for bringing new product designs through to manufacturing. Structural engineering contributes unique, in-depth technical knowledge to solve complex engineering problems in concert with multi-disciplinary teams including Industrial Design, Hardware Engineering, and Operations. Key job responsibilities You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will: · Perform analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Develop, analyze and test thermal, acoustic and structural solutions; from concept design, feature development, product architecture, through system validation · Support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Use simulation tools like Abaqus for analysis and design of products · Validate design modifications using simulation and actual prototypes · Use of programming languages like Python and Matlab for analytical/statistical analyses and automation · Establish noise thresholds for usability and compliance requirements · Determine and validate structural performance under use and test conditions · Have strong knowledge of various materials such as heat spreaders solutions to resolve thermal issues, damping materials for noise and vibration suppression · Use various data acquisition systems with thermocouples, accelerometers, strain gauges and IR cameras · Collaborate as part of the device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives · Design and execute tests using statistical tools to validate analytical models, identify risks and assess design margins · Create and present analytical and experimental results · Develop and apply design guidelines based on project results
US, NY, New York
We are seeking an Research 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
IN, KA, Bengaluru
We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke. As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Results Quality and Search Autocomplete. We are looking for a Applied Scientist II to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. You will build systems that anticipate search query intent and surface the right results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality search results. You will: - Develop and deploy ML models to produce relevant search results. - Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for ranking and relevance. - Develop reinforcement learning and reward-modeling approaches to continuously improve search results quality. - Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance. - Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment. - Lead end-to-end science projects from problem formulation through production launch, collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.
IN, KA, Bengaluru
We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke. As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Results Quality and Search Autocomplete. We are looking for a Applied Scientist II to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. You will build systems that anticipate search query intent and surface the right results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality search results. You will: - Develop and deploy ML models to produce relevant search results. - Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for ranking and relevance. - Develop reinforcement learning and reward-modeling approaches to continuously improve search results quality. - Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance. - Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment. - Lead end-to-end science projects from problem formulation through production launch, collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.
US, CA, Sunnyvale
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! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.
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, WA, Bellevue
Build the scientific intelligence layer powering Amazon’s satellite manufacturing system. As an Applied Scientist, you will develop machine learning models that transform fragmented manufacturing, test, quality, and operational data into actionable intelligence that improves how satellites are built. You will tackle ambiguous, high-impact problems where data is incomplete, noisy, and distributed, and where model outputs influence real-world manufacturing decisions. Your work will power AI-enabled workflows such as non-conformance disposition, root-cause analysis, and predictive test optimization - reducing defects, accelerating production, and helping create more intelligent, data-driven manufacturing systems. 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 - Translate ambiguous manufacturing and operational problems into well-defined scientific problems, modeling approaches, and evaluation criteria - Design, train, and deploy machine learning models, including LLM-based systems, retrieval models, and task-specific models - Develop and evaluate models using large-scale, noisy, heterogeneous datasets with incomplete, delayed, or imperfect ground truth - Apply state-of-the-art techniques in areas such as anomaly detection, root-cause inference, multimodal learning, information retrieval, and generative AI, adapting or extending them to meet project requirements - Design experiments and evaluation frameworks that capture real-world failure modes, distribution shift, and decision risk - Make principled tradeoffs among model complexity, data quality, accuracy, latency, cost, and maintainability - Build production-quality scientific components with appropriate testing, documentation, monitoring, and operational mechanisms - Work with Manufacturing, Quality, Test, and engineering partners to understand customer needs and translate them into effective scientific solutions - Analyze model and system performance, identify gaps and root causes, and iteratively improve deployed solutions - Clearly document scientific approaches, experimental results, design decisions, and lessons learned so that others can understand and reproduce the work - Contribute to technical discussions, mentor less experienced teammates, and help advance scientific and engineering best practices within the team A day in the life You may start by partnering with Quality and Manufacturing teams to define a training dataset for a root-cause prediction model, including how historical cases should be labeled and evaluated. You then design experiments and train models, comparing approaches across architectures, features, and data slices. Later, you analyze benchmark results to identify failure modes, data-quality issues, and generalization gaps, and refine the evaluation set to better represent real-world cases. You work with engineers to integrate the model into a production workflow, adding testing, monitoring, and feedback mechanisms. Throughout the day, you balance scientific rigor with practical constraints such as data availability, latency, reliability, and operational cost. About the team Leo Satellite Build Systems is the centralized AI team within Leo Production Operations. We build shared capabilities for AI across Production Operations, including governed data assets, machine learning models, retrieval systems, evaluation frameworks, and knowledge services. We work on real-world systems where scientific decisions can influence physical outcomes. We value rigorous experimentation, strong data foundations, clear documentation, and production-ready engineering. Our team is helping enable AI-native manufacturing by turning fragmented operational knowledge and data into reliable intelligence that improves production outcomes.