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June 3 - 7, 2026
Denver, Colorado
Conference
CVPR 2026

Overview

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses.

Sponsorship Details

Platinum
Booth 349
Conference Website
Related content

Accepted publications

Workshops

CVPR 2026 Workshop on Grounded Retrieval and Agentic Intelligence for Vision-Language (GRAIL-V)
June 3
Website: Link

Accepted Papers:
"CoCoA-DVC: Consistency and Concept Aware Training for Dense Video Captioning", presented by Jay Nitin Paranjape, Yue Guo, Sankar Venkataraman, Vishal M. Patel, Nataraj Jammalamadaka

"HTEF: Holistic Brand-Theme Alignment Scoring as a Catalog Gate for Grounded Conversational Recommendation", presented by Mahmudur Rahman, Dhruv Garg, Rishabh Rathod, Sanket Bindle

"Learning to Mix Flat and Curved Representations for Vision-Language Retrieval", presented by Kathy Wu, Sarthak Srivastava

"RAGENT: Robust Optimization for Grounded Vision-Language Retrieval", presented by Kathy Wu, Sarthak Srivastava

"ViSS-R1: Self-Supervised Reinforcement Video Reasoning", presented by Bo Fang, YuXin Song, Haoyuan Sun, Xinyao Zhang, Qiangqiang Wu, Wenhao Wu, Antoni B. Chan

Location: Room 506

Description: A CVPR 2026 workshop for researchers and practitioners building grounded multimodal retrieval, reranking, and verification systems that can be deployed with confidence.
12th International Workshop on Computer Vision in Sports (CVsports) at CVPR 2026
June 4
Website: Link

Poster Presenter: Kevin Song

Location: Room 503

Description: Computer vision has recently started to play an important role in sports, in particular for performance optimization and analytics, and in media productions, where computer vision-based graphics in real-time enhances different aspects of the game. The potential of computer vision algorithms in sports is huge, ranging from automatic annotation of broadcast footage, through to better understanding of sport injuries, coaching, and enhanced viewing. The ambition of this workshop is to bring together practitioners and researchers from different disciplines to share ideas and methods on current and future use of computer vision in sports.
2nd Workshop on Video Large Language Models
June 4
Website: Link

Organizing Committee:

General Chairs: Larry Davis, Rene Vidal, Son Tran, Vimal Bhat, Garin Kessler, Kushan Thakkar, Jayakrishnan Unnikrishnan

Program Chairs: Rohit Gupta, Swetha Sirnam, Bhagyashree Puranik

Location: 3A-3D

Description: The VidLLMs Workshop focuses on the latest advancements and challenges of Video Large Language Models. We aim to bring together researchers and practitioners from academia and industry to discuss open problems, applications, and future directions in this space. Join us at CVPR 2026 for a full-day event exploring the latest in Video Large Language Models and engage with leading experts, participate in challenge tracks, and discover the future of video understanding.
CVPR 2026 Workshop on Medical Reasoning with Vision Language Foundation Models
June 4
Website: Link

Invited Speakers: Maria Xenochristou

Location: Room 110

Description: The CVPR 2026 Workshop on Medical Reasoning with Vision Language Foundation Models (Med-Reasoner) aims to bring together computer vision researchers, medical AI experts, imaging scientists, and practicing clinicians to discuss state-of-the-art advancements, applications, and challenges in reasoning capabilities for medical vision-language models. The workshop will foster discussions that inspire innovation in interpretable medical AI and address real-world deployment challenges including privacy constraints, workflow integration into healthcare systems, and ensuring fairness across patient populations. Through invited talks from leading researchers at Google DeepMind, Stanford, MIT, and University of Toronto, contributed paper presentations, interactive poster sessions, and expert panel discussions, we will establish reasoning architectures and evaluation frameworks that advance healthcare applications with the potential to impact millions of patients globally.
CVPR 2026 Workshop on Personalization in Generative AI
June 4
Website: Link

Oral Presentation #1: "MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer", presented by Michael Opitz

Location: Room 4CD

Description: The P13N: Personalization in Generative AI workshop aims to unite researchers, practitioners, and artists from academia and industry to explore the challenges and opportunities in personalized generative systems.

Generative AI has revolutionized creativity and problem-solving across domains, yet personalization remains one of the most challenging and underexplored frontiers. Building systems that understand and adapt to individual users’ preferences, identities, or contexts raises profound technical, ethical, and societal questions. Through invited talks, panel discussions, poster sessions, and hands-on challenges, P13N serves as a platform to foster new directions in model design, evaluation, and governance for personalized generative systems.
The Seventh Annual Embodied Artificial Intelligence Workshop
June 4
Website: Link

Challenge Organizers: Xiaofeng Gao

Location: Room 107

Description: The goal of the Embodied AI workshop is to bring together researchers from computer vision, language, graphics, and robotics to share and discuss the latest advances in embodied intelligent agents. EAI 2026’s overaching theme is World Models for Embodied AI: embodied AI agents that create models of the world to help them imagine and act, or to help researchers to test and evaluate them. This umbrella theme is divided into three topics:
  • World Models for Action and Evaluation Explores both dynamics models which incorporate physics and geometry, and video models where dynamics are implicit.
  • The Resurgence of Classic Methods Examining new applications of techniques such as reinforcement learning and model-predictive control to embodied AI.
  • Long-Horizon Embodied Intelligence Explores benchmarks and methods for multi-step tasks, robust testing, and, in particular, safe operation.
VAND 4.0 Challenge at CVPR'26
June 4
Website: Link

Co-organizers of VAND 4.0 Challenge: Sebastian Höfer, Dorian Henning, Anton Milan


Invited Speaker: "Visual Defect Detection in Retail Logistics: The Kaputt Dataset and VAND 4.0 Retail Challenge", presented by Sebastian Höfer at 3:30 - 4:00 pm

Location: Room 601

Description: Our workshop challenge aims to showcase current progress in anomaly detection across different practical settings while addressing critical issues in the field. Building on the encouraging results from previous years — including the VAND 3.0 challenge — this edition sets its sights even higher, pushing the boundaries of robust and generalizable anomaly detection models for real-world use cases, for the first time including both industrial and retail logistics focused competitions.

Booth Schedule

Friday, June 5
June 5
Demos

10:30 - 11:00am - "CompAgent: An Agentic Framework for Visual Compliance Verification" and "MARBLE: Multi-Agent Retrieval via Belief-Propagation and Fine-Grained Language-Vision Evidence", presented by Chun-Hao Liu and Rahul Ghosh

11:00 - 11:30am - "Amazon Photos: Chat with Photos", presented by Raja Bala

1:30 - 2:00pm - "Apollo: Agentic Marketing Creative for E-Commerce", presented by Kathy Wu

2:00 - 2:30pm - "The Kaputt Dataset and the Visual Anomaly and Novelty Detection (VAND) Challenge: Advancing the State of the Art in Visual Defect Detection", presented by Sebastian Hoefer and Dorian Henning


Come chat with us about:

10:30 - 11:00am - 3D Reconstruction, 3D Scene Understanding, Computer Vision, Efficient AI, Generative AI, Image Processing, LLM/VLM Post-Training (SFT and RL), Restoration and Enhancement, Sensor Fusion, Vision Language Models

11:00 - 11:30am - Agentic AI, Anomaly Detection, Computer Vision, Fraud Prevention, Large Language Models, Multi-Agent Trajectory Classification and Prediction, Robotics, Sports Analytics, Vision Language Models

1:00 - 1:30pm - AI and Creativity, Computer Vision, Multimodal Large Language Models, Video Machine Learning, Vision Language Action, Vision Language Models

2:00 - 2:30pm - Artificial Intelligence, Computer Vision, Evaluation, Generative AI, Health AI, Large Language Models, Machine Learning, Multimodal Machine Learning, Reinforcement Learning, Robotics, Speech Processing and Synthesis, Trust & Safety, Video Understanding and Synthesis, Vision Language Models

4:00 - 4:30pm - Diffusion, Efficient Vision Language Models, Large Language Models, Multimodal Image, Video Understanding, Unified Vision Language Models, Visual Agents
Saturday, June 6
June 6
Demos

1:30 - 2:00pm - "Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent", presented by Wenliang Zhong

2:00 - 2:30pm - "Vulcan Stow: A Robot With a Sense of Touch", presented by Bhavya Goyal

3:30 - 4:00pm - "Science Internships @ Amazon Informational", presented by Ankita Goyal (Science Recruiter for Amazon University Talent Acquisition)

4:00 - 4:30pm - "Demonstrating the Innovation Done in Prime Video", presented by Vimal Bhat



Come chat with us about:

10:00 - 10:30am - Computational Photography, Computer Vision, Diffusion, Large Language Models, Novel Sensing and Camera, Robot Perception, Unified Vision Language Models, Vision Language Model Post-Training

11:30 - 12:00pm - Audio-Visual Modeling, Computer Vision, Multimodal LLMs, Multi-Agent Trajectory Classification and Prediction, Multimodal Foundation Models, Multimodal Understanding and Generation, Post-Training, Speech-to-Speech, Sports Analytics, System Optimization

1:30 - 2:00pm - Computer Vision, Foundational Models, Image Generation, Multimodal Large Language Models, Multimodal Learning, Object Detection/Classification, Video Understanding, Video Vision New Advancements

2:00 - 2:30pm - Biomedical LLMs, Computer Vision, Generative AI, Machine Learning, Metric Learning, Protein Engineering, Robotics

3:00 - 3:30pm - Concept Personalization, Flow Matching/Diffusion Model, Image/Video Generation Editing, Reinforcement Learning

3:30 - 4:00pm - Computer Vision, Diffusion Models, Generative Models
Sunday, June 7
June 7
Demos

2:00 - 2:30pm - "Vision at the Edge: The AI Behind Amazon's Smart AR Delivery Glasses", presented by Yelin Kim


Come chat with us about:

10:00 - 10:30am - Computational Photography, Computer Vision, Novel Sensing and Camera, Road User 3D Object Detection/Tracking/Prediction, Robot Perception, Vehicular Bird's-Eye View Models, Vehicular On-Board Machine Learning & Computer Vision

Work with us

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Principal, Applied Scientist, AWS Applied AI Solutions
US, WA, Seattle
Join us if you're excited about pushing the boundaries of what's possible in physical AI working with world-class scientists and engineers and seeing your innovations deployed at unprecedented scale. We are seeking an exceptional Principal Applied Scientist to drive technical innovation in visual reasoning foundation models. You will be a technical leader who sets the research direction, architects novel solutions, and delivers breakthrough results that advance the state of the art while solving real-world business problems. You will lead the efforts of building a next-generation visual reasoning engine powered by frontier Large Video Models (LVMs). Your mission is to build a system that rivals human understanding of the physical world-moving far beyond the static perception of detection and tracking into the realm of deep spatial-temporal reasoning. This is not a passive computer vision tool; it is an agentic collaborator capable of interpreting natural language instructions navigating unstructured environments and executing complex tasks. You will sit at the high-stakes intersection of LVMs, LLMs, and Agentic AI engineering systems that don't just 'see' but reason and act within the physical world. You will own end-to-end technical solutions from research to production deployment driving innovation through hands-on research prototyping and deployment while delivering production impact. Key Responsibilities • Direct the technical vision for next-gen visual reasoning pioneering the use of LVMs to solve high-dimensional spatial-temporal problems • Design and implement novel deep learning architectures combining a multitude of modalities including image video and geospatial data • Solve computational challenges to train foundation models at scale taking advantage of latest developments in hardware and deep learning libraries • Architect scalable solutions that deliver real-time insights across diverse physical environments • Build agentic AI systems that autonomously execute end-to-end workflows transforming visual data into actionable business intelligence • Collaborate with multiple science and engineering teams to build adaptations that power use cases across diverse domains • Publish research at top-tier conferences (CVPR NeurIPS ICML) and establish technical thought leadership in visual reasoning and multi-modal AI • Mentor scientists and engineers while maintaining significant hands-on contribution to technical solutions • Influence product roadmaps through deep technical expertise and business acumen Key job responsibilities - Direct the technical vision for next-gen visual reasoning, pioneering the use of LVMs to solve high-dimensional spatial-temporal problems - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data - Solve computational challenges to train foundation models at scale, taking advantage of latest developments in hardware and deep learning libraries - Architect scalable solutions that deliver real-time insights across diverse physical environments - Build agentic AI systems that autonomously execute end-to-end workflows, transforming visual data into actionable business intelligence - Collaborate with multiple science and engineering teams to build adaptations that power use cases across diverse domains - Publish research at top-tier conferences (CVPR, NeurIPS, ICML) and establish technical thought leadership in visual reasoning and multi-modal AI - Mentor scientists and engineers while maintaining significant hands-on contribution to technical solutions - Influence product roadmaps through deep technical expertise and business acumen A day in the life • Develop and implement novel foundation model architectures working hands-on with data and our extensive training and evaluation infrastructure • Guide and support fellow scientists in solving complex technical challenges from spatiotemporal reasoning to efficient multi-task learning • Guide and support fellow engineers in building scalable and reusable infrastructure to support model training evaluation and inference • Lead focused technical initiatives from conception through deployment ensuring successful integration with production systems • Drive technical discussions with the team and key stakeholders • Conduct experiments and prototype new ideas • Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Amazon 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. 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. 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 in the cloud. Here at AWS, 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.
Member of Technical Staff - Science, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff to drive foundational research and build intelligent robotic systems from the ground up. In this role, you will operate at the intersection of cutting-edge AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale. We’re looking for researchers who think from first principles, push the boundaries of what’s possible, and take full ownership of turning breakthrough ideas into working systems.  You will join the next revolution in robotics, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence and independent research initiatives in areas such as locomotion, manipulation, perception, sim2real transfer, multi-modal, multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You’ll have the freedom to pursue ambitious research directions while leveraging Amazon’s vast computational resources to tackle ambiguous problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, sim-to-real transfer, as well as foundation models focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to team's technical decisions and influence implementation strategies to help shape our approach to next-generation robotics challenges - Mentor fellow researchers while maintaining solid individual technical contributions A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges across the full robotics stack - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions and brainstorming sessions with team leaders, fellow researchers and key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster and extensive robotics infrastructure - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Member of Technical Staff - Hardware Science, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff to drive foundational research and build intelligent robotic systems from the ground up. In this role, you will operate at the intersection of innovative AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale. We’re looking for researchers who think from first principles, push the boundaries of what’s possible, and take full ownership of turning breakthrough ideas into working systems. You will join the next revolution in robotics, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll develop breakthrough foundation models that enable robots to perceive, understand, and interact with the physical world in unprecedented ways—with a strong emphasis on the hardware systems that bring these models to life. You'll drive independent research initiatives in areas such as locomotion, manipulation, motor control, actuator design, sim2real transfer, and multi-modal robot learning, designing novel frameworks that bridge state-of-the-art research with real-world hardware deployment at Amazon scale. In this role, you'll balance innovative technical exploration with hands-on hardware implementation, collaborating with mechanical, electrical, and controls engineering teams to ensure your models and algorithms perform robustly on physical robotic platforms in dynamic real-world environments. You'll have access to Amazon's computational resources and advanced robotics infrastructure—including high degree-of-freedom prototype platforms, custom actuators, and precision sensing systems—enabling you to tackle ambitious problems in areas like multi-modal robotic foundation models, motor-level control optimization, and efficient model architectures that scale across diverse robotic hardware. Key job responsibilities - Drive independent research initiatives across the full robotics stack, including robot co-design, manipulation mechanisms, innovative actuation and motor control strategies, state estimation, low-level control, system identification, reinforcement learning, and sim-to-real transfer, as well as foundation models for perception and manipulation - Lead full-stack robotics projects from conceptualization through hardware deployment, taking a system-level approach that integrates actuator dynamics, sensor feedback (force/torque, IMUs, encoders), and electromechanical constraints with algorithmic development - Develop and optimize control algorithms and sensing pipelines for physical robotic hardware, including motor characterization, actuator performance tuning, and robust sensor integration in production environments - Collaborate with hardware, mechanical, and electrical engineering teams to ensure seamless integration of learned models across the robotics stack—from embedded compute and communication buses to actuator-level control - Contribute to the team's technical strategy and help shape our approach to next-generation hardware-aware robotics challenges, including hardware-in-the-loop validation and prototype-to-deployment transitions A day in the life - Design and implement innovative systems and algorithms, leveraging our extensive computational and robotics hardware infrastructure to prototype and evaluate at scale - Collaborate with hardware and software engineers to solve complex technical challenges spanning motors, actuators, sensors, and learned control - Lead technical initiatives from conception to hardware deployment, working closely with robotics engineers and lab teams to integrate your solutions into physical robotic platforms - Participate in technical discussions and design reviews with team leaders, hardware engineers, and fellow scientists - Leverage our compute cluster and advanced robotics lab—including high-DoF prototype platforms and custom actuation systems—to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that perform reliably on real-world robotic hardware About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through ground breaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Sr. Applied Science Manager, Perfect Order Experience (POE) AI
US, WA, Seattle
The Perfect Order Experience (POE) AI team combines artificial intelligence, machine learning, and economic insights to ensure exceptional customer experiences and seller success on Amazon. We develop advanced scientific solutions that protect product authenticity, maintain quality standards, and safeguard intellectual property across Amazon's vast catalog. Our work spans from building detection systems using state-of-the-art Large Language Models to creating automated investigation processes and risk treatment mechanisms. Our solutions directly impact billions of customer interactions and enable millions of sellers to thrive while maintaining the highest standards of trust and quality. We are seeking an exceptional Senior Applied Science Manager to lead key AI initiatives to ensure a perfect order experience for Amazon customers. In this role, you will spearhead the development of a domain specific large language model designed to comprehend complex seller behaviors and relationships. You will lead the research and implementation on LLM pre-training, fine-tuning and reinforcement learning for LLM reasoning. You will implement and influence ranker models that intelligently adjust product visibility based on risk signals and trust metrics. Key job responsibilities - Drive AI strategy and lead a team of applied scientists in developing ML solutions. - Lead the end-to-end development of a domain specific LLM. - Drive the development of large-scale pre-training and post-training strategies for the LLM using domain-specific datasets. - Architect automated risk detection and treatment systems that combine multi-modal signals to identify product quality issues and implement optimization-based mitigation strategies. - Collaborate with other science teams to develop/ influence ranker models that optimize product visibility. About the team About the Perfect Order Experience (POE) AI Team The POE AI Science team sits at the forefront of Amazon's efforts to ensure customers can shop with confidence. Our team combines artificial intelligence, machine learning, and economic insights to protect product authenticity, maintain quality standards, and safeguard intellectual property across Amazon's vast catalog. The work we do directly impacts billions of customer interactions and enables millions of sellers to thrive while maintaining the highest standards of trust and quality.
Applied Science Manager III, RBKS AI
CA, ON, Toronto
The RBKS AI team is responsible for innovating AI features for Ring and Blink cameras, with a mission to make our neighborhoods safer. We are working at the intersection of computer vision, generative AI (GenAI), and ambient intelligence. The team is seeking Applied Science Manager to lead initiatives that combine advanced computer vision and multimodal GenAI capabilities. This role offers a unique opportunity to lead a world-class team while shaping next-generation home security technology and advancing the field of AI algorithms and systems. The team is focused on productizing research in computer vision and GenAI into products that benefit millions of customers worldwide, such as real-time object detection, video understanding, and multimodal LLMs. We are at the forefront of developing AI solutions that seamlessly blend into our products while respecting privacy, delivering unprecedented levels of intelligent security experience. Key job responsibilities - Lead and guide a team of applied scientists in designing and developing advanced computer vision and GenAI models and algorithms for comprehensive video understanding, including but not limited to object detection, recognition and spatial understanding - Drive technical strategy and roadmap for privacy-preserving CV and GenAI models and systems, ensuring the team delivers efficient fine-tuning and on-device and in-cloud inference solutions - Partner with product and engineering leadership to translate business objectives into technical roadmaps, and ensure delivery of high-quality science artifacts that ship to products - Build and maintain strategic partnerships with science, engineering, product, and program management teams across the organization - Recruit, mentor, and develop top-tier applied science talent; provide technical and career guidance to team members while fostering a culture of innovation and excellence - Set technical direction and establish best practices for AI products/features across multiple projects and initiatives
Applied Scientist III, RBKS AI
CA, ON, Toronto
The RBKS AI team is responsible innovating AI features for Ring and Blink cameras, with a mission to make our neighborhood safer. We are working in the intersection of computer vision, generative AI (GenAI), and ambient intelligence. The team is seeking AI Applied Scientists to work on initiatives that combine advanced computer vision and multimodal GenAI capabilities. This role offers a unique opportunity to shape next-generation home security technology while advancing the field of AI algorithms and systems. The team is focused on productizing research in computer vision and GenAI into products that benefit millions of customers worldwide, such as real-time object detection, video understanding, and multimodal LLMs. We are at the forefront of developing AI solutions that seamlessly blend into our products while respecting privacy, delivering unprecedented levels of intelligent security experience. Key job responsibilities * Design and develop advanced computer vision and GenAI models and algorithms for comprehensive video understanding, including but not limited to object detection, recognition and spatial understanding * Develop privacy-preserving CV and GenAI models and systems, focusing on efficient fine-tuning and on-device and in-cloud inference * Map product requirements into science solutions and deliver high-quality science artifacts that ship to products * Collaborate with scientists, engineers, product/program managers and other cross-functional teams * Provide technical leadership on AI products/features, and develop and mentor junior scientists on the team.
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