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.

Latest news

The latest updates, stories, and more about Alexa Prize.
US, CA, Sunnyvale
We are seeking an Applied Scientist II to work on development of an AI-based data intelligence and classification platform that will redefine how security and privacy assessments and enforcement are conducted at scale. This mission-critical platform will leverage AI-driven autonomous agents to conduct proactive, intelligent security operations across the company. The platform will integrate deeply with internal security, privacy, engineering, and cloud-native tools to provide self-serve, automated insights, verifications, and enforcement mechanisms. This role requires strong technical expertise in AI/ML, LLMs, and distributed cloud infrastructure, as well as thought leadership to drive alignment across multiple teams, customers, and business units. This is an opportunity to shape the future of AI-driven security and privacy assurance at an enterprise scale, defining standards, influencing company-wide security posture, and leading technical innovation at the highest level. Key job responsibilities * Architect and define the next-generation data classification and search matching platform, leading the technical strategy for AI-driven security automation across applied science and engineering teams * Build on multi-agent LLM framework, and influence your organizations in adopting the promising approaches * Develop a highly scalable, traditional ML-based as well as LLM-based intelligent security agent framework that enables internal teams to automate processing of structured and unstructured data * Combine depth and breadth of domain expertise and provide technical leadership to the entire team while also doing hands-on work by diving deep into details to diagnose complex system performance problems. About the team The Data Categorization team helps Amazonians understand their data and govern it at scale and ensures experiences delivered by Amazon to our customers uphold our high security and privacy standards. The science team harnesses AI to strengthen Amazon’s privacy and security posture more efficiently and effectively.
GB, London
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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. Why Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
US, CA, Sunnyvale
Are you excited about developing state-of-the-art Machine Learning, Natural Language Processing, Deep Learning and Computer Vision algorithms and designs using large data sets to solve real world problems? Do you have proven analytical capabilities and can multi-task and thrive in a fast-paced environment? Do you want to build a foundation for your career after your Master's or Ph.D program at an industry-leading company? You enjoy the prospect of solving real-world problems that, quite frankly, have not been solved at scale anywhere before. Along the way, you’ll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver—someone who truly enables machine learning to create significant impacts. As an Applied Scientist, you will bring statistical modeling and machine learning advancements to data analytics for customer-facing solutions in complex industrial settings. You will be working in a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products
US, WA, Seattle
We are seeking a senior Applied Scientist to join a science team within Amazon Customer Service that is reimagining how customers are connected with the right support experience. This is a senior technical leadership role where you will drive scientific strategy, mentor junior scientists, and deliver foundational models that power real-time personalization at scale. Key job responsibilities - Lead the science for understanding the demand side of customer service interactions - Characterize who is contacting us, what they need, and how complex their situation is - Invent novel representation learning approaches that encode customer interactions - Directly inform intelligent routing decisions across hundreds of millions of annual contacts
US, WA, Seattle
We are seeking an Applied Scientist to join a science team within Amazon Customer Service that is reimagining how customers are connected with the right support experience. This is a role at the intersection of representation learning, behavioral modeling, and large-scale experimentation where you will see your science deployed in production systems serving real-time decisions. Key job responsibilities - Develop machine learning models that capture how service associates perform across different problem types and how their capabilities evolve over time - Power intelligent routing decisions across hundreds of millions of annual customer contacts - Directly improve both customer experience and associate satisfaction through model-driven decisions - Work at the intersection of representation learning, behavioral modeling, and large-scale experimentation - Deploy science into production systems serving real-time decisions
US, CA, Sunnyvale
Amazon's PRISM team is seeking an innovative Applied Scientist to build the intelligence layer powering the Catalog Diagnostic Assistant — a conversational AI agent that unifies Amazon's fragmented catalog diagnostic experience into a single natural-language interface. Rather than switching between legacy tools, internal users ask CDA a question in plain language and CDA coordinates across data sources, reasons through complex diagnostic workflows, and returns a combined answer with source citations. This role sits at the intersection of Generative AI, agentic architectures, and large-scale information retrieval applied to the world's largest product catalog. You will design and build the scientific core of an agent that autonomously investigates catalog anomalies — diagnosing why products aren't live, why attributes aren't publishing, or why matching decisions went wrong — across billions of products, petabytes of multimodal data, and dozens of marketplaces. You will be the founding scientist for the CDA product, defining the research agenda for agentic diagnostics, developing novel approaches to skill-based reasoning and tool orchestration, and owning the full lifecycle from problem formulation through production deployment at Amazon scale. You will pioneer advanced GenAI solutions that power next-generation agentic experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog and tackle problems at the frontier of AI research. Key job responsibilities - Formulate open research problems at the intersection of GenAI, agentic reasoning, and large-scale catalog diagnostics — defining how an autonomous agent should decompose, investigate, and explain complex catalog issues - Design and develop novel agentic architectures (skill planning, tool selection, multi-step reasoning, chain-of-thought verification) that enable CDA to autonomously resolve diagnostic workflows that traditionally required manual expert investigation - Build and optimize retrieval-augmented generation (RAG) systems over Amazon's catalog data, ensuring the agent retrieves the right evidence from the right data sources to ground its diagnostic answers - Advance the science of efficient model deployment — developing distillation, compression, and LLM serving optimization strategies that preserve diagnostic reasoning quality in production-grade architectures while reducing latency and cost - Make frontier models reliable for autonomous decisions — advancing uncertainty calibration, confidence estimation, and interpretability methods so CDA's agentic diagnoses can be trusted at scale - Own the full research lifecycle from problem formulation through production deployment — designing rigorous experiments, iterating rapidly, and seeing your research directly improve diagnostic accuracy and coverage - Partner closely with CDA engineers to translate research prototypes into production systems serving thousands of daily diagnostic sessions - Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact - Mentor engineers on advanced ML/GenAI techniques, experimental design, and scientific rigor About the team We are a GenAI science team within Amazon's Selection and Catalog Systems (ASCS) organization. Our mission is to advance state-of-the-art Generative AI to deeply understand, uniquely identify, and intelligently diagnose every product at Amazon scale. We push the boundaries of multimodal LLMs, agentic systems, and generative AI to solve foundational catalog challenges — from product identity and semantic matching to automated catalog diagnostics and intelligent catalog analytics. Our scientists work across multiple high-impact problem spaces: establishing canonical product identity across billions of items, powering next-generation agentic experiences for catalog operations, and building AI systems that make Amazon's catalog self-diagnosing and self-healing. We operate at the frontier of AI research applied at unprecedented scale — petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity. This is a collaborative, fast-moving environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your ideas at scale, and publish your findings at top venues.
US, CA, San Francisco
We are seeking a Member of Technical Staff — Mechanical Engineer to lead the structural and electromechanical design of advanced high degree-of-freedom robotic systems within a frontier AI and robotics research lab. You will own the mechanical architecture of our robotics platform end-to-end: actuator selection, joint design, thermal management, cable routing, sensing integration, and structural optimization — all under the demanding constraints of a high-DoF system that must be lightweight, robust, and production-viable. This is a unique opportunity to define the physical design direction of a next-generation robotics platform from first principles. The ideal candidate has deep experience designing complex robotic systems and scaling hardware from concept through prototyping to production. You thrive on analytical problem solving, possess strong intuition for structural and electromechanical tradeoffs, and are equally comfortable in CAD and on the shop floor. You will be the technical lead of a small team of talented mechanical engineers while collaborating closely with electrical, firmware, controls, and AI research teams to set the mechanical and structural direction of the platform. Beyond core platform development, this role offers real latitude to push into novel territory — novel actuation methods, advanced sensor integration, or new approaches to mechatronic packaging. If you have research instincts alongside shipping discipline, you'll find an environment that rewards both. We're looking for someone who wants to be the technical authority on how a high-performance robot is physically built, not just a contributor to the effort. What You Bring: - A systems-thinking mindset with a strong grasp of cross-domain engineering tradeoffs. - A bias toward action: comfortable building, testing, and iterating rapidly. - A collaborative and communicative working style — especially in multi-disciplinary research environments. - A passion for robotics and advancing the state of the art in intelligent, capable machines. Key job responsibilities - Lead mechanical design of robotic subsystems and full platforms, including structures, joints, enclosures, and mechanisms for a research environment. - Own kinematic, dynamic, and structural analyses to guide the design and optimization of full systems and subsystems of high-DoF robots - Specify and integrate actuators and motors for high-torque density applications in high-degree-of-freedom systems. - Drive rapid iteration and prototyping cycles — from concept sketches through functional hardware — to accelerate learning and compress development timelines in a fast-paced R&D environment. - Partner with the AI research team to establish simulation pipelines that enable rapid exploration of mechanical design space, informing actuator choices, structural topology, and system-level tradeoffs before committing to physical builds. - Contribute to thermal management strategies for motors, sensors, and embedded compute hardware. - Integrate sensors such as lidar, stereo cameras, IMUs, tactile sensors, and compute modules into compact, functional assemblies. - Design and route cabling and wire harnesses, ensuring reliability, serviceability, and thermal/electrical integrity. - Prototype and test mechanical systems; support hands-on builds, debug sessions, and field testing. - Conduct root cause analysis on system-level failures or performance issues and implement design improvements. - Apply Design for Manufacturing (DFM) and Design for Assembly (DFA) principles to transition prototypes into scalable builds (10s–100s of units). - Collaborate with cross-functional teams in electrical engineering, controls, perception, and research to meet research and product goals. About the team Frontier AI & Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.
US, WA, Seattle
Are you passionate about leveraging your applied science skills to deliver actionable insights that impact daily business decisions? Do you like using ML, GenAI, Causal Inference, Reinforcement Learning and Experimentation to answer challenging product and customer behavior questions? Do you want to be a technical leader and build flexible and global solutions for complex financial services/payments, risk, and causal problems? If so, here is a great opportunity to consider! Amazon B2B Payments & Lending is seeking an Applied Scientist II who will combine their technical expertise with business acumen to generate critical insights that will set the strategic direction of the business. You will be a thought leader on the team, help set the team's strategic focus and roadmaps, and design and build systems/solutions that support financial products, working closely with business/product partners and engineers. You will utilize ML/GenAI/Causal Inference/Reinforcement Learning/Experimentation methodologies, data and coding skills, problem solving and analytical skills, and excellent communication to deliver customer value. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!