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Malmo, Sweden
Conference
ECCV 2026
September 8 - 12, 2026
Malmö, Sweden

Overview

The European Conference on Computer Vision (ECCV) is a biennial premier research conference in Computer Vision and Machine Learning, managed by the European Computer Vision Association (ECVA).

Sponsorship Details

Platinum
Booth #3
Conference website

Main conference publications

Booth Schedule

Presentations @ Booth #03:

11:00 AM: DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models | Francesco Taioli, Iaroslav Melekhov

11:30 AM: Seeing, Understanding, and Deciding: Video Quality for Prime Video Live Events | Alex Mackin, David Higham 

12:00 PM: The Kaputt Dataset and the Visual Anomaly and Novelty Detection (VAND) Challenge: Advancing the State of the Art in Visual Defect Detection | Mariliza Tzes, Artemij Amiranashvili

4:00 PM: Contextual AI Pause Ads - AI-Generated Contextual Pause Ads | Sahir Karani, David Hsiao, Jethro Chu

Meet the Team @ Booth #03:

10:30 AM
Audiovisual Learning, Computer Vision, Robotics Perception, Video Quality Assessment

11:00 AM
Audiovisual Learning, Computer Vision , Robotics Foundation Models, Robotics Manipulation, Robotics Perception, SLAM, Video Quality Assessment, World Model

11:30 AM
Audiovisual Learning, Computer Vision, Diffusion, Foundational Models, Generative AI, Image/Video Generation, Multimodal Understanding, VLM, Video Quality Assessment

12:00 PM
AI, Computer Graphics, Computer Vision, ML, 3D Vision, Visual Content Generation

4:00 PM
Computer Vision, Contextual AI Pause Ads, Generative Models, LLM Post Training + Evaluation, Multimodal Foundation Model, Recommender Systems, Robotics

4:30 PM
3D, Autonomous Systems, Computational Imaging, Scene Understanding, Simulation, Photogrammetry, Video Quality

Presentations:

10:30 AM: 3D Semantic Scene Understanding from Autonomous Drone Imagery | Andrea Porfiri Dal Cin, Martin Lenz

11:00 AM: Towards AI Rapper: Creating Interactive Rap Battle Experience with Generative AI | Aiham Taleb, Zainab Afolabi

11:30 AM: Contextual AI Pause Ads - AI-Generated Contextual Pause Ads | Sahir Karani, David Hsiao, Jethro Chu 

12:00 PM: Overview of JWO Research | Kostia Robert

4:00 PM: Benchmarking Evidence‑Aligned Multimodal Reasoning |Louise Xie

Meet the Team @ Booth #03:

10:30 AM
Computer Vision, Machine Learning, Multimodal Foundation Model, Robotics Perception, Video Quality

11:00 AM
Agent Scaffolding, Anomaly Detection, Computer Vision, LLM, Machine Learning, Robotics Perception, Self-Supervised Learning

11:30 AM
Agents/LLMs, Computer Vision, Contextual AI Pause Ads, Generative AI, LLM Post Training + Evaluation, Machine Learning, Mobile Robotics, Multimodal Understanding, Recommender Systems, Task and Motion Planning, VLMs

12:00 PM
Foundational Models, Multimodal Perception, Representation Learning, Robotics, Robotics Perception, Scene/Object Understanding, Vision-Language Model

4:00 PM
3D, Autonomous Systems, Computational Imaging, Computer Vision, Generative Models, Photogrammetry, Robotics, Scene Understanding, Simulation, Video Quality

Presentations:

10:30 AM: Foundation Model for Robotic Manipulation Deployed At Scale for Warehouse Automation | Vikedo Terhuja, Kaiwen Wang

11:00 AM: Seeing, Understanding, and Deciding: Video Quality for Prime Video Live Events | Alex Mackin, Josh Anderson

11:30 AM: Towards AI Rapper: Creating Interactive Rap Battle Experience with Generative AI | Aiham Taleb, Zainab Afolabi

Meet the Team @ Booth #03:

10:30 AM
3D Computer Vision, Computer Graphics, Robotics, Robotics Perception

11:00 AM
Computer Vision, Machine Learning, Robotics Perception

11:30 AM
Computer Vision

3:00 PM
3D, Autonomous Systems, Computational Imaging, Computer Vision, Machine Learning, Photogrammetry, Scene Understanding, Simulation

Work with us

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Applied Scientist II, Alexa Ads
IN, KA, Bengaluru
Alexa+ is the world’s best Generative AI powered personal assistant / agent for consumers, and is becoming the conversational AI interface for Amazon services with the launch of Alexa for Shopping on Amazon.com and Amazon mobile app. At Alexa Ads, we are creating industry's first and most advanced Agentic Advertising products to drive Agentic Commerce. We are seeking an Applied Scientist to join our newly expanding team in India focused on Alexa Agentic/Conversational Ads and Personalization. In this role, you will build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions. You will work closely with other scientists, engineers, and product managers to take models from conception to production. Key job responsibilities - Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization. - Conduct hands-on data analysis and build scalable ML pipelines. - Design and run A/B experiments to measure the impact of new models on customer experience and ad performance. - Collaborate with software development engineers to deploy models into high-scale, real-time production environments. About the team We are building a new science team in Bangalore to solve some of the most impactful problems in computational advertising. This isn't about tweaking existing models as we are rethinking how ads are ranked, priced, and personalized across voice-first and screen-first surfaces. These are problems that don't have textbook solutions. Key points to note about the team: 🧪 Greenfield team - you are not joining a mature org with rigid processes. You will shape the science roadmap, pick the problems, and define the culture from day one. 📈 Direct business impact — your models directly drive revenue. No yearly cycles to see if your work matters. 🌏 Global scope, local autonomy — collaborate with scientists and engineers across Seattle, Sunnyvale, and Bangalore, but own your problem space end-to-end. 🎓 Ship AND Publish: We encourage top-tier publications (NeurIPS, ACL, EMNLP, KDD, ICML, WWW) while ensuring your research hits production.
Sr Data Scientist, Amazon - Vertical Ads
US, CA, Palo Alto
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Sr Data Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As a Data Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
Applied Scientist I
IN, TS, Hyderabad
Are you passionate about solving complex problems with machine learning and scientific rigor? As an Applied Scientist I at Amazon, you will translate real-world business challenges into well-defined scientific problems and build solutions that directly benefit customers. You will work alongside experienced scientists and engineers, applying your expertise in areas such as natural language processing, computer vision, or robotics to design experiments, develop models, and deliver production-ready code. This is a role where your curiosity and technical depth will drive meaningful impact from day one. Key job responsibilities - Design, develop, and implement machine learning models and algorithms to solve well-defined business problems, mapping business goals and metrics to scientific approaches and evaluation criteria. - Write secure, stable, testable, and maintainable production code, applying state-of-the-art data structures and algorithms while following software development best practices at a high quality bar. - Conduct rigorous experiments to evaluate model performance, benchmark results against current research, and iterate on solutions to improve accuracy and customer outcomes. - Collaborate with team members to scope technical approaches, communicate findings through internal research reports, and contribute to peer-reviewed publications when aligned with business needs. - Stay current with research trends in your area of expertise, champion the adoption of recent scientific advancements, and help onboard and mentor scientist interns. A day in the life You might start your morning reviewing experiment results from a model you trained, analyzing performance metrics and identifying areas for improvement. After a design discussion with your team, you refine your approach and push updated code for review. In the afternoon, you read a recent research paper recommended by a senior scientist, exploring whether a new technique could improve your current solution. You wrap up by documenting your methodology so teammates can understand and build on your work. About the team Our team is focused on applying scientific methods and machine learning to solve problems that matter to Amazon's customers. We value rigorous experimentation, clear communication, and a collaborative environment where scientists at every stage of their career can grow. We are building toward solutions that push the boundaries of what is possible, and we are looking for curious, thoughtful scientists who want to contribute to that mission and learn alongside a supportive group of peers.
Applied Scientist I, Worldwide Returns & Recommerce - Science
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team! Key job responsibilities * Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. * Use SQL to query and analyze the data. * Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. * Use machine learning and analytical techniques to create scalable solutions for business problems. * Research and implement novel machine learning and statistical approaches. * Mentor interns. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. About the team When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models.
Sr. Communication Systems Research Scientist – Radio Resource Management
US, TX, Austin
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3GPP-compliant service to unmodified commercial handsets. The Role: Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Research, design and specify RRM algorithms for Amazon Leo’s 3GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services. • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO). • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum. • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity. • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation. • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior. • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures. • Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment. • Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.
Sr. Communication Systems Engineer
US, CA, San Diego
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. Come work at Amazon! The Role: Be part of the team defining the overall communication system and architecture of Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in integration, verification and testing of the systems especially focused on MAC and higher layer testing. This role will also be responsible developing and testing advanced L1/L2/L3 concept to improve the performance and reliability of the LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. In this role you will: - Work within a project team and take the responsibility for the Leo’s communication system design, system integration and verification. - Work as a part of the team in building a suite of system and network simulation services in Matlab / C++ / Python - Develop requirements from system level to HW/SW level and define test cases associated with the requirements. - Identify additional HW and SW that are needed for the purposes of verification and guide the HW/SW development team in the development of these test solutions/tools// - Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements - Write scripts / code for functions / features required for specific simulation, testing and verification of given RF system EXPORT CONTROL REQUIREMENTS Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
PhD Economics Intern – Structural IO, Summer 2027
US, WA, Seattle
Amazon Economics is seeking Structural IO Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available STRUC internships in 2027. Key job responsibilities As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: - Analyze large-scale datasets using structural econometric techniques to solve complex business challenges - Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions - Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization - Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value - Build and refine comprehensive datasets for in-depth structural economic analysis - Present complex analytical findings to business leaders and stakeholders
Senior Economist, Litigation and Regulatory Economics
US, VA, Arlington
Want to help Amazon tell its customer-centric story around the world and work in a highly cross-functional environment with economists, lawyers, scientists, public policy, public relations, and business teams? If yes, keep reading! You'll join a team of economists, engineers, and lawyers to develop economic analysis and evidence supporting legal and regulatory matters across all our lines of business worldwide—including retail, marketplace services, AWS, consumer experience, shopping and search, and operations. In this role, you will have exposure to complex regulatory issues that are of high strategic importance to the company and will develop significant expertise on the economics of Amazon’s business operations and the industries in which it operates. If you're an economist with a passion for the current legal and policy debate, strong practical judgment and creative problem-solving skills, a love of communicating economic ideas to non-technical audiences, a knack for distilling data and economic models into key insights, and a track record of delivering results fast, we want to talk to you! Key job responsibilities • Provide data-driven guidance on high-stakes legal and regulatory questions facing Amazon worldwide • Collaborate with economists, scientists, engineers, and non-technical partners on high-impact projects with global scope • Partner with global public policy teams to apply economic analyses to current policy debates on competition, AI, and related issues • Engage with external stakeholders to drive deeper understanding of Amazon’s business model and the value it develops for the economy • Support requests for economic analyses and data in ongoing regulatory and litigation matters worldwide • Synthesize business facts and data into compelling economic narratives, translating complex findings into actionable insights • Advise stakeholders across Amazon on a broad spectrum of complex and often novel economic issues • Conduct, direct, and coordinate all phases of research projects—defining key questions, evaluating methodology, executing analysis, and communicating results
PhD Economics Intern – Reduced Form Causal Inference, Summer 2027
US, WA, Seattle
Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this a full-time internship (40 hours per week, with hourly compensation). You'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available RFCA internships in 2027. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders
Senior Applied Scientist, FBA AI Science & Analytics
US, WA, Bellevue
FBA AI Science and Analytics accelerates the AI-native transformation of Fulfillment by Amazon by building, integrating, and scaling AI-powered data & science products and seller-facing experiences that drive operational efficiency and growth across Fulfillment by Amazon globally. We learn seller behaviors, design the policies and incentives that shape their experience, and ship science products that help third-party sellers grow topline and cut operating costs at Amazon scale. Our work sits at the intersection of machine learning, statistics, economics, operations research, and GenAI/LLMs. We're looking for a Senior Applied Scientist who wants to put GenAI to work on a hard, high-visibility problem: building next-generation multi-agent systems that interact with millions of sellers and guide them through their toughest challenges at scale. You'll own solutions spanning supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, harness engineering, and reinforcement learning. The ambition is to make AI a native layer in every seller decision rather than a separate tool sellers must adopt, delivering actionable insight in minutes, not days. You'll shape end-to-end experiences across the highest-frequency seller workflows, including inventory optimization, inbound efficiency, defect improvements, reimbursements, and capacity planning. The role carries direct visibility with senior Amazon business leaders and works together with fellow scientists, engineers, and product teams to launch production-grade agentic capabilities. Key job responsibilities - Design, build and deploy FBA’s GenAI architectures end to end. - Apply state-of-the-art ML and GenAI solve diverse business problems across seller supply chain systems. - Define the team’s long-term science vision and roadmap, driven fundamentally from our customers' needs, translating those directions into specific plans for scientists, engineers, and product partners. - Partner closely with scientists and software engineers to drive real-time model implementations and deliver high-impact features. - Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model evaluation and model implementation. - Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers
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