As an ML Engineering Intern, you'll support the perception and learned-behavior teams, developing code for automation and working with real field data on projects involving model training, optimization, and simulation.

The role involves designing and deploying Vision-Language-Action models for outdoor autonomy, developing robust data pipelines, optimizing models for edge deployment, and collaborating with multiple teams for integration and evaluation.

Design, develop, and integrate mechanical, electrical, and control systems for autonomous UGVs; collaborate across disciplines; support prototyping, testing, validation, field debug, component selection, and documentation while ensuring safety and EMC compliance.

Develop and optimize manufacturing processes and quality systems for autonomous UGVs. Create work instructions, lead root-cause analysis, qualify suppliers, develop test and validation plans, support prototypes and production transitions, and drive continuous improvement to meet safety and regulatory standards.

Design, optimize, and manage supply chain processes for autonomous UGV components and assemblies. Evaluate and manage suppliers, implement quality assurance, monitor inventory and demand forecasts, troubleshoot disruptions, ensure compliance, and drive continuous improvement for cost, lead time, and resilience while supporting prototype-to-production transitions.

Lead development and maintenance of safety and compliance requirements for autonomous UGV platforms. Perform risk assessments, hazard analyses, and safety validation (simulation, prototypes, field tests). Specify and qualify components, support prototype builds and root cause analysis, document compliance, and assist with certifications (e.g., CE).

Drive maturation of 0-to-1 prototypes into reliable, manufacturable products at small production scale. Refine designs for DFM/DFA, develop BOMs and test protocols, troubleshoot prototype builds, work with suppliers and contract manufacturers, validate designs through testing and field deployments, and maintain engineering documentation while ensuring regulatory and quality compliance.

Design, integrate, and validate end-to-end systems for autonomous UGVs: develop architectures, lead requirements and risk analysis, execute verification/validation, support prototypes and field deployments, and ensure safety and regulatory compliance.

The Senior Robotics Software Engineer will develop and maintain simulation environments for testing autonomous systems, ensuring accurate modeling and integration with CI workflows.

The role involves building and owning core platform infrastructure for autonomous robots, focusing on middleware, device integration, and cloud infrastructure. Responsibilities include managing the robotics platform stack, developing CI/CD pipelines, and driving architectural decisions.

The Senior MLOps Engineer will manage the ML lifecycle, build data pipelines, ensure data quality, oversee labeling operations, and collaborate cross-functionally.

As a Senior Computer Vision Engineer, you will develop and optimize computer vision and machine learning algorithms for robotics systems, while collaborating with cross-functional teams and maintaining code documentation.

The Firmware Engineer will develop, test, and maintain embedded firmware for autonomous UGV hardware, collaborating with cross-functional teams and optimizing firmware for sensors and control systems.

The Head of People will develop and execute a people strategy, enhance company culture, manage talent lifecycle, and support HR practices.