KingFisher

KingFisher is a team of several graduate and undergraduate students from the University of Illinois, Urbana-Champaign led by faculty advisor Julia Hockenmaier.

The team's interests and experiences include computer vision, goal-oriented dialogue, multimodal learning, robotics and action planning, and real world applications of embodied NLP agents. Their vision for the project is a robot that can both understand and utilize the complexities of human language to interact with users and the environment in meaningful ways.

uofillinois_kingfisher_teamphoto.jpg
Location: Urbana-Champaign, Illinois
Faculty advisor: Julia Hockenmaier

Neeloy C. — Team leader

Neeloy is a second year PhD student in the Human-Centered Autonomy Lab at UIUC studying robotics and artificial intelligence. Some of his research works include applying reinforcement learning (RL) to dense robot crowd navigation, tackling sparse reward RL problems, vehicle anomaly detection, and human-robot interaction tasks in vehicle cockpits. Throughout his higher-level education, he has gained industry experience from companies such as Anheuser-Busch, Qualcomm, Brunswick, and Ford. He has also been a teaching assistant for the Introduction to Robotics class at UIUC, aiding students to learn the fundamentals of robotics in a laboratory setting. Neeloy is excited to apply what he has learned from other problem settings to the embodied AI task, and gain experience in computer vision and natural language processing.

Abhinav A.

Abhinav is a first year Statistics (Concentration: Analytics) Graduate student at UIUC with 5 years of experience at Verizon as a Data Scientist. He completed his undergraduate in Computer Science & Engineering (Concentration: AI) in 2016 from Lovely Professional University, India. He has worked as a DevOps Administrator, Applications Developer, Real Time Streaming Data Engineer along with experience in Data Science. In that space, he has worked on Predictive Modeling, NLP, Anomaly Detection, Sequence Mining, XAI, and CV. He has been a Microsoft Student Ambassador in 2014-2015 and a AI6 city ambassador of Hyderabad, India in 2018.

Blerim A.

Blerim is a 3rd-year undergraduate studying computer engineering at UIUC and a recent transfer from the College of DuPage. His research interests include computer vision and perception with applications in robotics. His main areas of expertise include embedded systems and electronics with applications in robotics and IoT. He has worked as an embedded security intern at Pacific Northwest National Laboratory creating machine learning models for network security within IoT devices. He has also led the development of a mining robot for the NASA Lunabotics Competition which leveraged ROS, Realsense cameras and various other electronics.

Peixin C.

Peixin is an Electrical and Computer Engineering Ph.D. student at UIUC in the area of Robotics and Artificial Intelligence. His research interests are embodied language understanding, robotics, and reinforcement learning. His works involve developing embodied vision-based spoken language understanding agents for robotic systems using reinforcement learning. He also has experience in learning-based robotic navigation in both static and dynamic environments. He is familiar with robotic simulation and has designed and created multiple OpenAI Gym environments based on PyBullet and AI2Thor. He is also familiar with deep learning packages such as TensorFlow and Pytorch.

Haomiao C.

Haomiao is an undergraduate student at UIUC studying statistics, computer science and physics. He is interested in machine learning, robotics, NLP and computer vision. Haomiao has experience working on computer vision projects focusing on 3D structure reconstruction. Haomiao also has experience with implementing, training, and optimizing different machine learning models. Haomiao has some previous experience in NLP, applying semi-supervised learning in language classification. Haomiao is interested in all kinds of NLP models and applications and is willing to learn and explore more through the project.

Runxiang (Sam) C.

Sam is a third year Computer Science PhD student at UIUC. I work on machine learning, currently focusing on multimodal learning. Previously, Sam researched on reliability of distributed systems, specifically on misconfiguration-related failure prevention. Sam obtained a Bachelor of Science in Computer Science from UC Davis in 2019, where he worked on multimodal machine translation, conversational AI, and software data analysis.

Jongwon P.

Jongwon is an undergraduate student at UIUC majoring in Computer Science. Jognwon's interest lies in the intersection of NLU and Multimodal Learning, envisioning weakly supervised models that assimilate the functionalities of the brain. I am passionate about the attention mechanism employed by transformers and their applications outside language tokens. Jognwon's prior experience includes creating a BERT model that simulates the day-and-night continual learning process of the brain for text summarization. Outside the ML research, Jognwon develops websites (fullstack) and deploys ML strategies for quantitative trading in the cryptocurrency space.

Devika P.

Devika is a third-year undergraduate at UIUC majoring in Computer Science. She has previously interned at Motorola Solutions as a Software Engineering Intern working on their predictive analytics team developing machine learning algorithms for mission-critical radio networks. Devika has also interned at Apple on the Siri Product team within their ML organization working on optimizing resources for best performance using data analytics and ML models. Previously, her research interests have included packet-scheduling in high criticality networks and theoretical topology

Nikil R.

Nikil is an undergraduate studying Mathematics and Computer Science at UIUC. His research interests are primarily in natural language processing and computer vision. He has experience working on multiple NLP sub-areas such as topic modeling, semantic similarity, keyphrase extraction and generation, and text embeddings. He also has experience training,
testing, optimizing and deploying deep learning models (involving computer vision and time series forecasting) using the power of high performance computing, with applications to diverse domains including astrophysics, spectroscopy and cancer research. In addition, he has some familiarity with AWS, having used it in projects involving NLP and machine learning.

Risham S.

Risham is a Computer Science PhD student at UIUC in the area of Artificial Intelligence. Her current interests are grounding and multimodal networks and she is working on a similar goal-oriented dialogue task on a Commander model on the Minecraft Dialogue Corpus. She also has experience working on a range of NLP projects including evaluating the faithfulness of grounded representations and their training dynamics within VQA, information extraction from scientific papers, annotating, creating, and updating datasets, and text classification and generation.

Kulbir S.

Kulbir is currently pursuing a PhD at UIUC where he works to integrate the Alexa API with Agricultural robots to enable remote voice control of CNC based gardening robots. Kulbir's interest in Robotics was fostered during undergraduate studies in Electrical Engineering. During Kulbir's masters in robotics at the University of Maryland, he built a solid theoretical foundation in computer science and robotics, by taking core robotics courses focussing on Planning and Perception for Autonomous Systems, ROS and Decision Making.

Julia Hockenmaier — Faculty advisor

Julia Hockenmaier is a full professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Her main area of research is computational linguistics or natural language processing.

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ES, M, Madrid
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IN, KA, Bengaluru
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IN, KA, Bengaluru
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GB, London
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US, CA, San Francisco
The Models, Quantum, and Silicon (MQS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Software Engineer to join our growing Software team. You will work closely with our experimental physics teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, data analysis pipelines, calibration nodes) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with experimental physicists to develop scientific software workflows that enable scaling to larger quantum devices. Inclusive Team Culture Here at Amazon, 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 conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon 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. Key job responsibilities - Architect extensible & intuitive frameworks for running quantum computing experiments and analyzing data. - Leverage the latest techniques in quantum calibration to enable scaling to larger devices. - Optimize the performance of experiment & analysis tools to enable faster experiment throughput. - Develop dashboards that allow experimentalists to inspect and control the state of quantum device calibration. - Deploy and maintain cloud infrastructure that supports increasingly-complex science workflows. - Empower scientists to actively contribute to the codebase through mentorship and documentation. We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As a Research Software Engineer embedded in a broader research science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life The majority of your time will be spent on projects that extend the functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of our science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the MQS Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Within Sponsored Products, the Bidding team is responsible for defining and delivering a collection of advertising products around bid controls (dynamic bidding, bid recommendations, etc.) that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities As a Senior Applied Scientist on this team, you will: • Lead a new initiative across Sponsored Products Bidding focused on AI/ML based features. • Be the technical leader in AI, Machine Learning; lead efforts within this team and across other teams. • Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. • Drive end-to-end AI/Machine Learning projects that have a high degree of ambiguity, scale, complexity. • Build models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your AI/ML models. • Run A/B experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative AI/ machine learning approaches. • Recruit Applied Scientists to the team and provide mentorship. A day in the life Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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. The SPB Bidding team within Sponsored Products and Brands is focused on guiding and supporting Millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in bidding systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware bidding system that leverages auction simulations, ML models, and optimization algorithms. This framework, will operate across SPB bidding system and proactively delivering value based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art bidding agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning and preference optimization), ensuring our systems are both scalable and adaptive.
US, WA, Bellevue
What does it take to build a foundation model that can forecast demand for hundreds of millions of products — including ones that have never been sold before? At Amazon, our Demand Forecasting team is tackling one of the most ambitious challenges in applied time series research: building large-scale foundation models that generalize across an enormous and diverse catalog of products, geographies, and business contexts. This is not incremental modeling work. We are redefining what's possible in demand forecasting. Our team operates at a scale that is unmatched in industry. We run experiments across millions of products simultaneously, pushing the boundaries of what foundation models can learn from vast, heterogeneous time series data. We are also exploring novel data generation techniques that augment our already unprecedented dataset — opening new frontiers in model generalization and forecasting for products with limited or no sales history. The models you build here will ship to production and directly influence hundreds of millions of dollars in automated inventory decisions every week, labor plans for tens of thousands of employees, and Amazon's financial outlook. Beyond operational impact, this team contributes to the broader scientific community and advances the state of the art in time series foundation models. If you are a scientist who wants to work at the frontier of time series research, at a scale no academic lab or startup can match, and see your work deployed to real-world impact — this is the team for you. Key job responsibilities - Design and run rigorous experiments at scale to evaluate and improve foundation model performance across hundreds of millions of products, geographies, and business verticals - Lead the end-to-end lifecycle of forecasting models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout - Conduct online and offline labs to measure the real-world impact of forecast improvements beyond accuracy, including downstream supply chain, inventory, and financial outcomes - Develop and deploy production-grade deep learning and statistical models using Python, Scala, SQL, and related tools - Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform model development - Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels - Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues A day in the life No two days look the same, but most will involve some combination of deep technical work, cross-functional collaboration, and scientific thinking at a scale you won't find anywhere else. You might start the morning reviewing the results of an experiment running across hundreds of millions of products — analyzing whether a new foundation model variant is improving generalization on cold-start items, or whether a novel data generation approach is meaningfully shifting forecast quality. You'll dig into the numbers, form a hypothesis, and design the next iteration. Later in the day, you could be in a stakeholder review, walking business and engineering partners through a set of launch metrics — explaining not just forecast accuracy, but the downstream supply chain and financial impact your model is driving. Getting a model to production at Amazon requires rigor: you'll define success criteria, run online and offline labs to validate real-world impact, and build the case for sign-off across technical and business stakeholders. You'll write code — Python, Scala, SQL — to process and analyze data at a scale most scientists never encounter. You'll collaborate closely with scientists, engineers, and business teams, and contribute to research that has a real chance of being published and advancing the field. The work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships — this is where you do it. About the team The Demand Forecasting team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost — for hundreds of millions of customers around the world. Our mission is to push the frontier of what's possible in large-scale time series forecasting, and to deploy that science where it creates real, measurable impact. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish — we ship. And we don't just ship — we measure, iterate, and raise the bar. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.
US, WA, Seattle
Are you interested in leading growth initiatives for one of Amazon’s most significant and fastest growing businesses? Selling Partners offer hundreds of millions of unique products and are a critical to delivering on our vision of offering the Earth’s largest selection and lowest prices. The Amazon Marketplace enables over 2 million third-party selling partners in eleven marketplaces to list their products for sale to Amazon customers across the world. Within our WW Marketplace business, International Seller Services (ISS) oversees the recruiting and development of Selling Partners for all of our international marketplaces (e.g. UK, Germany, Japan, Middle East etc.). ISS also enables global selling, helping Sellers in one country expand and sell internationally. Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, the Central Science Team of Amazon's International Seller Services has an exciting opportunity for you as an Applied Science Manager. We are seeking an experienced science leader who is adept at a variety of skills; especially in generative AI, computer vision, and large language models that will help international sellers succeed as they sell on Amazon. The right candidate will provide science leadership, establish the right direction and vision, build team mechanisms, foster the spirit of collaboration and innovation within the org, and execute against a roadmap. This leader will provide both technical direction as well as manage a sizable team of scientists. They will need to be adept at recruiting, launching AI models into production, writing vision/direction documents, and building team mechanisms that will foster innovation and execution. Additionally, while the position is based in Seattle, this leader will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Key job responsibilities Key job responsibilities Responsibilities include: * Drive end-to-end applied science projects that have a high degree of ambiguity, scale, complexity. * Provide technical / science leadership related to NLP, computer vision and large language models. * Research new and innovative machine learning approaches. * Recruit high performing Applied Scientists to the team and provide mentorship. * Establish team mechanisms, including team building, planning, and document reviews. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact.
US, VA, Arlington
Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world! Passionate about building, owning and operating massively scalable systems? Want to make a billion-dollar impact? If so, we have an exciting opportunity for you. The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions; improving the availability, reliability, latency, performance and efficiency to operate AWS regions. The AWS Managed Operations Intelligence (MOI) Team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insight strategy for AWS. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration, ETL, model development and data visualization. You will leverage a diverse set of tools and technologies, including general analytical frameworks (Spark, Airflow, etc.), AI frameworks (Hugging Face, etc.) and various machine learning frameworks, to tackle complex business problems. Your analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform AWS' Operations and Site Reliability Engineering teams. You will work on ambiguous and complex business and research science problems at scale. You are and comfortable working with cross-functional teams and systems. This role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs. Our employees and the neighboring community will also benefit from the associated investments from the Commonwealth including infrastructure updates, public transportation improvements, and new access to Reagan National Airport. By working together on behalf of our customers, we are building the future one innovative product, service, and idea at a time. Are you ready to embrace the challenge? Come build the future with us. This position requires that the candidate selected be a U.S. citizen. 10012 Key job responsibilities - Work with large and complex data sets to solve a wide array of challenging problems using different analytical approaches - Develop ML/AI models. Partner with software teams to productionalize these models. - Data Pipeline and Infrastructure: design and implementation of data pipelines - Metric Development and Monitoring: Define and develop advanced, customized metrics and key performance indicators (KPIs) that capture the nuances of the organization's strategic objectives and operational complexities. Continuously monitor and evaluate the performance of metrics A day in the life Why AWS? Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. 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. About 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 Infrastructure Services (AIS) AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. 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. 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. About the team The Managed Operations Intelligence (MOI) Team helps AWS operate its services across the world. We help monitor AWS operations by providing insights and recommendations on AWS operations. This position requires that the candidate selected be a U.S. citizen.
GB, London
Amazon Strategic Account Services (SAS) Tech Organization is looking for an Applied Scientist Applied Scientist who can autonomously drive scientific innovations from research to production, developing sophisticated AI solutions that serve both Amazon's global seller base and internal Marketplace Consultants. Working in a highly collaborative environment, you'll leverage expertise in machine learning, operations research, and statistics to translate theoretical advances in LLMs, probabilistic modeling, and optimization into practical applications. The role demands strong capabilities in prototyping and iterative improvement, bridging cutting models with real-world applications while maintaining scientific rigor and measurable business impact. Key job responsibilities - Lead the development of sophisticated AI solutions leveraging deep learning, LLMs, and advanced machine learning techniques to transform both seller operations and internal consultancy capabilities at scale - Define and drive long-term scientific vision for the organization, translating complex business challenges into innovative technical solutions that advance the state-of-the-art in applied machine learning - Design and implement advanced ML architectures combining multiple learning paradigms - from reinforcement learning and causal inference to predictive modeling - to tackle critical marketplace challenges - Architect next-generation recommendation and optimization systems that handle complex multi-dimensional constraints while maintaining robustness and interpretability at scale - Drive end-to-end development of AI applications from research through production, collaborating with engineering teams to ensure successful deployment and conducting rigorous A/B experiments to validate impact - Pioneer novel applications of foundation models and generative AI, developing sophisticated evaluation frameworks while maintaining Amazon's high standards for accuracy and reliability - Lead technical discussions across organizational boundaries, effectively communicating complex scientific concepts to diverse stakeholders while staying at the forefront of ML/AI research advancements About the team What is Amazon Strategic Account Services (SAS)? The SAS team aims to accelerate the full potential of our Sellers, helping them to navigate the increasing complexity of the e-commerce space. Our team provides in-depth strategic consultancy using a data-driven, collaborative, and a Customer-focused approach to achieve commercial goals of Amazon Sellers.