AI Autonomous Systems Engineer Jobs

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Collaborate with the world model team to build a state-of-the-art training and simulation platform for robotics. Develop persistent 3D/4D scene representations that maintain temporal consistency. Unlock advanced robotics planning and decision making through in-house, cutting-edge world models. Ensure sensing and system dynamics perform reliably in high-stakes, real-world operations where models think, simulate, and act. Partner with ML researchers to innovate on generative models and physical AGI. Collaborate closely with ML researchers developing multimodal world models and generative systems, and with hardware teams and partners to ensure robotic platforms, sensing, and system dynamics behave optimally in real-world operation.

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Contribute to the technical development and integration of advanced robotic automation solutions for manufacturing automation, utilizing the Intrinsic platform, ROS and state-of-the-art AI capabilities. Collaborate closely with research and industry partners to successfully integrate AI and automation capabilities into factory settings. Document designs, processes, and results, communicating effectively with internal technical teams and partners.

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As a Robotics Application Engineer specializing in Intelligent Manufacturing Automation, the responsibilities include closely collaborating with the automation group and industry partners to advance manufacturing automation by extending capabilities and solutions. Daily tasks involve integrating and developing robotics solutions, including processes, software features, and integrating state-of-the-art AI for manufacturing automation. The role includes working with the team leading the deployment of impactful robotic systems in production. Additional responsibilities entail contributing to the technical development and integration of advanced robotic automation solutions for manufacturing automation using the Intrinsic platform, ROS, and AI capabilities, collaborating with research and industry partners to integrate AI and automation into factory settings, and documenting designs, processes, and results while communicating effectively with internal technical teams and partners.

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As a Senior State Estimation Engineer at Hayden AI, the responsibilities include deriving and implementing novel real-time pose estimation algorithms, researching, developing and implementing algorithms to solve large-scale mapping and similar problems, collaborating with other engineers on multi-sensor calibration both in-situ and in-factory, programming and developing software in C++ and/or Python, contributing to high impact multidisciplinary projects across teams, collaborating with deep learning, device, and cloud teams to improve overall system architectures, and providing mentorship to junior engineers.

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As a Senior Modeling & Simulation Engineer, responsibilities include developing models and infrastructure for the integrated simulation pipeline in C++, designing deterministic, high-performance simulation tools capable of faster-than-real-time execution for development, testing, and release, implementing test scenarios and writing unit, system, and regression tests. Collaborate across autonomy, embedded, GNC, and test engineering teams to ensure the simulation mirrors real aircraft behavior and mission scenarios. Contribute to platform-agnostic simulation tooling to accelerate future development efforts. Perform verification and validation (V&V) analysis on model tools. Conduct system performance analysis and generate reports and visualizations. Utilize best practices in C++, simulation architecture, and performance engineering.

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Lead Zoox’s technical strategy for prognostics across vehicle systems, focusing on reducing in-service failures and improving fleet availability. Identify and prioritize failure modes where prognostics can create meaningful operational value based on failure behavior, detectability, warning horizon, and serviceability. Develop and manage prognostics concepts, methodologies, and technical requirements for monitoring degradation, predicting remaining useful life, and detecting pre-failure behavior in fielded systems. Partner with reliability, design engineering, service, firmware/software, and data teams to define signals, features, infrastructure, and product changes needed for effective prognostics. Translate field performance, repair history, usage patterns, and failure analysis into monitor strategies and deployable health indicators with Design Reliability and Field Reliability. Guide development, validation, and tuning of prognostic models and health monitoring algorithms using field and test data. Establish technical frameworks for evaluating prognostic performance including sensitivity, false positive burden, lead time, robustness, and operational usefulness. Drive tradeoff decisions between prognostics, diagnostics, inspection intervals, and design improvement based on risk, cost, and implementation practicality. Build data and analysis architecture to support prognostics at scale including data quality, feature generation, monitor traceability, and performance feedback loops. Partner with service operations to ensure prognostics outputs translate into actionable maintenance decisions, clear workflows, and measurable business value. Provide technical leadership and mentorship across the prognostics workstream to raise methods, rigor, and cross-functional execution. Communicate recommendations, risks, and roadmap priorities to engineering leadership and stakeholders.

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Design, build, and deploy robotic and embedded software that powers advanced pilot assistance systems in production environments. Own autonomy-related features or subsystems from concept through deployment, emphasizing reliability and performance. Write, review, and maintain high-quality Python and C++ code across autonomy, systems, and embedded components. Integrate software with hardware, sensors, and perception or data ingestion pipelines to support autonomous and operator-in-the-loop decision-making. Optimize software for edge compute environments, managing CPU/GPU usage, latency, and implementing appropriate safety mechanisms and fail-safes. Lead testing, validation, and deployment efforts to ensure systems meet safety-critical and mission-critical requirements. Mentor engineers and contribute to technical direction through design reviews, code reviews, and hands-on collaboration.

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Lead the development, integration, and deployment of the AI pilot to various defense and commercial platforms, overseeing a multidisciplinary team including software engineers, autonomy developers, and perception engineers to ensure successful integration and optimization for each platform's needs. Responsible for technical strategy, team leadership, and delivery of scalable, reliable autonomy to meet program needs. Lead technical leadership and strategy by managing the integration of AI autonomy, perception, and control algorithms with platform-specific hardware and software systems, establishing best practices for software quality, safety, reliability, performance, and scalability, and championing innovation through research and applied AI/ML techniques. Build, mentor, and retain a diverse and high-performing team while fostering a culture of technical excellence, quality, and accountability, coordinating cross-disciplinary efforts for seamless AI integration within flight systems. Drive engineering execution and technical quality by collaborating with engineers to enhance AI systems, translating business needs into technical plans, owning technical quality outcomes, enforcing engineering standards, implementing development processes, and leading continuous improvement initiatives. Ensure AI pilot customization for specific platform needs, leading platform-specific integration efforts with control systems, sensors, and communication interfaces. Collaborate cross-functionally to align platform requirements with AI capabilities and system design and lead performance optimization and testing through rigorous simulation and flight system tests to validate and ensure AI performance meets or exceeds mission requirements.

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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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As a System Architect, you own the end-to-end architecture, system definition, and strategic implementation for the entire portfolio of robotic and autonomous defense systems, collaborating closely with executive leadership and technical leads and forming a partnership with the Product Manager. Responsibilities include translating complex strategic goals into system-of-systems designs, defining and championing system architecture strategy across the enterprise, ensuring all systems are correctly sized and verified through simulations and system sizing, guiding major technical investment decisions, coordinating large multidisciplinary engineering organizations, providing technical leadership across mechanical, electrical, software, GNC, ML, and product teams, governing system integration standards and validation processes, managing specification and architecture reviews, and implementing processes to improve requirements traceability, documentation, and validation workflows across engineering.