AI Perception Engineer Jobs

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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Lead multidisciplinary teams in autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution while balancing hands-on technical oversight with performance optimization, innovation, and stakeholder communication. Design and implement advanced perception algorithms for object detection, classification, and multi-target tracking across diverse sensors. Integrate data from vision systems, radars, and other sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness. Develop and refine state estimation algorithms for localization and pose estimation using IMU, GPS, vision, and other sensing inputs. Interpret sensor ICDs and technical specifications to ensure proper data handling and synchronization. Optimize perception pipelines for performance, robustness, and real-time efficiency in simulation and real-world environments. Collaborate closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules. Validate algorithms using synthetic data, simulations, and field testing. Coordinate with hardware and sensor teams to integrate perception algorithms with onboard compute platforms and sensor payloads. Drive innovation in airborne sensing techniques for unmanned aircraft operating in complex or contested environments. Travel approximately 10-15% of the year to office locations, customer sites, and flight integration events.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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Architect and evolve Gecko Robotics' ROS2-based control framework and planning systems for articulated manipulators. Develop perception-driven motion planning using visual and other sensor inputs. Design closed-loop inspect → analyze → rework workflows. Optimize robotic inspection throughput within active manufacturing lines. Own system-level integration between robot control stack, industrial hardware, and Gecko's inspection software. Support system deployment and validation in production environments.

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The role involves leading the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. It includes exploring the intersection of computer vision and robotic control to design systems that allow robots to perceive and interact with objects in dynamic environments. Responsibilities include creating models that integrate visual data to guide physical manipulation, collaborating with a multidisciplinary team to translate concepts into deployable robotic capabilities, researching and developing deep learning architectures for visual perception and sensorimotor control, designing algorithms for manipulating complex or deformable objects with precision, optimizing and deploying prototypes onto robotic hardware, evaluating model performance in simulation and real-world environments for robustness, identifying opportunities to apply advancements in computer vision and robot learning to industrial problems, and mentoring junior researchers while contributing to the technical direction of the research roadmap.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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As a SLAM / Multi-View Geometry Engineer on the Robotics team, you will develop systems that enable robots to perceive, track, and reconstruct the world in 3D from multi-camera and multimodal sensor data. You will work on real-time and offline SLAM pipelines used during teleoperation and robot data collection, as well as scalable systems for reconstructing and tracking 3D structure from large datasets. Specific responsibilities include developing and deploying online SLAM systems used during robotic data collection with multi-camera sensor stacks and teleoperation platforms, building systems for large-scale 3D reconstruction and point tracking across massive datasets, working with research and engineering teams to scale multi-view geometry pipelines to large datasets, improving the accuracy, robustness, and scalability of perception systems used in robotics data collection and training pipelines, and collaborating across robotics, perception, and ML teams to integrate geometry-based methods with learned models.

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As a Perception Engineering Intern at Hayden AI, the responsibilities include taking ownership of a real project and seeing it through to completion, building and shipping features with support from senior engineers, writing clean and scalable code, testing work and iterating quickly, being involved in all phases from design discussions to deployment, collaborating with engineers in code reviews and team discussions, participating in standups, sprint planning, and retrospectives, supporting the team on ad hoc engineering tasks, helping improve performance, reliability, or usability where needed, and asking questions, seeking feedback, and applying it quickly. Deliverables or project examples may include GPS data analysis, training deep learning models, creating AI datasets, lidar/camera data tooling, test cases for end-to-end system performance, developing a cloud service in the event processing pipeline, and adding a page or new user flow to the Portal web application.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation for sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Work with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.