AI Robotics Software Engineer Jobs

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As a Senior Autonomy Systems Test Engineer, you will oversee the development of extensive test plans, develop standardized simulated test design tooling processes to execute many different scenarios, validate end-to-end behaviors, and create triage pipelines for analyzing issues observed during offline and on-vehicle testing. You will participate regularly in in-vehicle testing missions to observe new features in real-world conditions, help triage and root cause issues found during testing, analyze test results to ensure no regression in existing functionality, and update testing processes to enable scalable QA for future milestones. Additionally, you will create test strategies and plans for self-driving behavior features, identify, track, report, and resolve testing issues with cross-functional software development teams, propose, prototype, and validate new automated testing methodologies for the AI stack, design, develop, and execute synthetic and log-based test scenarios on an in-house simulation framework, and compile 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.

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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. 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 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 enabling 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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Build and scale simulation frameworks for integrated testing of autonomy, GNC, and embedded systems in C++. Design deterministic, high-performance simulation tools capable of faster-than-real-time execution for development, testing, and release. Implement scenario simulation tooling and formal test infrastructure. Collaborate across autonomy, embedded, GNC, and test engineering to ensure the simulation mirrors real aircraft behavior and mission scenarios. Develop infrastructure for CI integration, parallel simulation execution, and automated regression testing. Profile, optimize, and validate C++ codebases for performance, determinism, and fidelity. Contribute to architecture decisions that define the next generation of aircraft simulation tools within Shield AI. Mentor engineers and guide best practices in C++, simulation architecture, and performance engineering.

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As an Electrical Engineer - Systems, you will work on Machine Learning/AI hardware systems projects to develop solutions for current and future data center deployments. Your responsibilities include working with the hardware team on test vehicle and bring up board design, evaluating end-to-end system design trade-offs. You will lead EE circuit level design and collaborate with power, thermal, and mechanical teams to drive AI hardware system design. The role requires working with product teams to ensure system goals are met and collaborating with ASIC/FPGA, Software, and Verification teams for proper verification of features. You will also engage with manufacturing teams to ensure designs are manufacturable and ready for volume production, support field teams for deployed systems, gather system requirements, define architecture, execute hardware design, product validation, lead system bring up, validation, NPI, deployment, and sustaining of hardware solutions. Cross-functional collaboration with Hardware, Software, Mechanical, Thermal, Validation, Manufacturing, and external vendors is essential, as is driving system development from concept through production, and leading debug and root cause analysis of deployed systems.

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The Production AI Ops Lead designs and develops the production lifecycle of full-stack AI applications while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. Responsibilities include owning the production outcome with full accountability for long-term performance and reliability of AI use cases across international government agencies; ensuring full-stack integrity by overseeing the end-to-end health of the platform and seamless integration between AI core and other components; building automated systems to monitor model performance and data drift across dispersed environments; managing the technical lifecycle within diverse regulatory frameworks; leading incident response for production issues in mission-critical environments and establishing preventive guardrails; translating technical performance metrics into clear insights for senior government officials; and partnering with Engineering and ML teams to influence future technical architecture and decisions based on field learnings.

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Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency. Build tooling and establish development best practices to manage and upgrade foundational libraries such as Nvidia driver, PyTorch, TensorRT, to improve ML developer experience and expedite debugging efforts. Collaborate closely with cross-functional teams including applied ML research, high-performance compute, advanced hardware engineering, and data science to define requirements and align on architectural decisions. Work across multiple ML teams within Zoox, supporting in- and off-vehicle ML use cases and coordinating to meet the needs of vehicle and ML teams to reduce the time from ideation to productionization of AI innovations.

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You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios, work on impactful projects, and collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems.

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As a Senior Software Engineer on the Pilots team, the responsibilities include delivering robust, thoroughly tested, and maintainable C++ code for edge and robotics platforms, designing, implementing, and owning prototype perception systems that may transition into production-grade solutions, constructing and refining real-time perception pipelines including detection, tracking, and sensor fusion, adapting and integrating ML and CV models for Hayden-specific applications, driving technical decision-making balancing prototyping speed with production readiness, collaborating with the Product team and cross-functional Engineering departments, and contributing to shared infrastructure, tooling, and architectural patterns as pilots mature into foundational products.