AI Robotics Software Engineer Jobs

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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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Develop and optimize computer vision algorithms for object detection, tracking, and 3D reconstruction. Process and analyze large-scale point cloud data for digital twin applications. Support the integration of robotics frameworks such as ROS into real-time systems. Test and improve system performance in diverse environments. Collaborate with a multidisciplinary team to prototype and deploy innovative solutions.

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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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The role involves developing and evolving the tooling ecosystem used by hardware engineers, including hardware compilers, IR transformations, simulation, debugging, and automation infrastructure. Responsibilities include building and improving software tooling to accelerate hardware team productivity, extending and integrating hardware compiler stacks and connecting them to design workflows, enhancing developer experience and reliability through reproducible builds, better error messages, faster iteration loops, and dependable CI and regression infrastructure. The engineer will work closely with designers and verification engineers to address pain points by creating durable tools, dive into RTL as needed to debug issues, validate tool output, and improve debuggability, and engage with gate-level views, synthesis results, and implementation artifacts when necessary. Additionally, they will help enable PPA optimization loops by building analysis and automation tools related to area, timing, and power tradeoffs to improve tooling impact on these outcomes.

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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.

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As a Senior Autonomy Systems Test Engineer, you will accelerate product development by helping developers build safe and reliable autonomous driving software. You will oversee the development of extensive test plans, develop standardized simulated test design tooling processes to execute various scenarios, validate end-to-end behaviors, and create triage pipelines for analyzing issues seen during offline and on-vehicle testing. You will participate regularly in in-vehicle testing missions to observe feature behavior in real-world conditions, help triage and root cause issues seen during testing, analyze test results to ensure no regression in existing functionality, and update testing processes for scaling QA efficiency. Your specific duties include creating test strategies and test plans for self-driving behavior features, identifying, tracking, reporting, and resolving test strategy, planning, or implementation issues with cross-functional software teams, proposing and validating new testing methodology for the AI stack using automated metrics, designing, developing, and executing synthetic and log-based test scenarios on an in-house simulation test framework, and compiling triage strategies and results from different QA validation platforms and pipelines.

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Develop tools for validation and regression testing of image sensors, image processing pipelines, and hardware and software integration. Perform lab and real-world camera data collection and data analysis. Participate in tuning of sensor parameters and image processing pipelines to optimize image quality. Troubleshoot camera and image quality issues observed on autonomous vehicles. Design new hardware and the necessary software for sensor range. Work with perception software team to assess end to end camera performance.