Jobs at MOTOR Ai

Lead the Software Integration and Robotics team developing autonomous driving systems. Define system architecture and integration strategy across perception, planning, and control; manage integration backlogs, releases, dependencies, and quality standards; and guide CI/CD, build tooling, coding practices, and safety requirements. The role combines hands-on C++ and embedded real-time robotics expertise with engineering leadership, coaching, cross-functional technical alignment, and people management.

Administer and evolve the corporate IT environment, including networks, servers, endpoints, Microsoft 365, Entra ID, virtualization, cloud, backups, and access management. Monitor and improve system security, performance, availability, and reliability; troubleshoot issues; perform maintenance and patching; and document configurations for NIS2 and ISO 27001 certification. The role requires independent, hands-on IT administration across Windows, Linux, networking, and infrastructure services.

Design and implement safe, efficient motion-planning and trajectory-generation algorithms for Level 4 autonomous vehicles. Optimize trajectories using real-time constraints, validate performance through simulation and real-world data, analyze edge cases, and integrate planning outputs with perception and software teams. The role requires strong C++ or Python skills, robotics frameworks, optimization methods, control theory, state estimation, simulation, and an understanding of functional safety and embedded systems.

Test automated vehicles on public roads and controlled areas, conducting maneuvers, edge-case testing, and regression checks. Prepare test sessions, analyze logs and observations, document structured findings, triage issues, identify root causes, and create reproducible tickets. Develop test plans and cases, maintain requirements traceability, verify testability, and improve testing processes, tooling, and automation.

Leads the strategy, architecture, and operation of scalable data platforms, storage, and pipelines. Builds and manages the data infrastructure team, ensures platform reliability and cost efficiency, and partners with engineering, AI, perception, and simulation teams. Establishes data security, access control, governance, versioning, monitoring, and compliance standards for very large data volumes.

Lead the Perception and Autonomous Systems team while remaining hands-on in C++ development. Set architecture and engineering standards for real-time software, ROS2 nodes, computer vision pipelines, and autonomous vehicle systems. Guide machine learning model integration, inference optimization, data pipelines, and deployment workflows. Mentor engineers, coordinate across teams, support SOTIF considerations, and participate in field testing and experimental validation.

Design, build, and maintain data pipelines supporting AI and machine learning systems. Integrate, version, monitor, and validate training and inference data; develop automation tooling; and manage data quality across large-scale hybrid infrastructure. Collaborate with AI Engineers, Perception, and Data Infrastructure teams while supporting production systems, on-call duties, data annotation, and external service providers.

Develop, train, evaluate, deploy, and optimize machine learning models for Level 4 autonomous driving. Integrate models into production software, optimize real-time performance, run experiments, and collaborate with data, perception, and simulation teams. The role requires strong Python, deep learning, reinforcement learning, production deployment, cloud/GPU, and software engineering skills, plus a valid driving license and English proficiency.

Lead and manage PR lifecycle, perform technical reviews for sensor interfaces and ROS2 packages, maintain branching and release hygiene, monitor CI/CD and build pipelines, support Docker/Kubernetes infrastructure, and resolve integration blockers across multi-functional teams.

Lead and maintain hybrid infrastructure: administer Kubernetes (EKS), optimize CI/CD with Terraform/Ansible, manage Data Lake training data, integrate NeoCloud providers, and implement monitoring with Grafana/Prometheus while consulting development teams.

Develop and integrate low-level sensor software for autonomous vehicles: implement/debug drivers and protocols, perform hardware/software integration and in-vehicle testing, collaborate on architecture and sensor requirements, document development, and support electrical/mechanical installation.