Are you a Robot Operating System (ROS) expert who thrives on making robots run more efficiently, reliably and securely We are seeking a highly skilled ROS Architect to define, configure, and optimize the software backbone of our next-generation Physical AI Brain.
In this role, you will move beyond standard implementation to advanced customisation from tuning system performance, minimizing latency, and ensuring robust and secured DDS (Data Distribution Service) configuration for real-world autonomy.
Key Responsibilities:
- ROS Architecture Design: Define and implement robust ROS architectures, including workspace structure, package management, and node communication paradigms.
- ROS Configuration and Client Library Modification: Configure ROS QoS policies and security features, runtime management (Executor), and modify/add client libraries.
- ROS Launch & Deployment: Develop, maintain, and containerize (Docker) complex launch configurations and parameter management systems.
- ROS Development & Integration: Design, code, test, and debug software nodes, services, and actions in C++ or Python to control Physical AI systems.
- Edge Compute OS Optimization: Customizing Linux kernel performance, scheduling, and resource allocation to ensure low-latency performance for robotics applications and other safety-critical tasks.
- System Diagnostics & Troubleshooting: Identifying and resolving low-level system failures, debugging race conditions, and handling memory management to improve stability.
- Middleware Development & Integration: Designing and managing the communication framework (e.g., DDS, MQTT) that allows nodes, sensors, and actuators to exchange data in real-time.
- Driver Development: Writing and optimizing device drivers for sensors (Lidar, cameras, IMUs) and actuators (motors, servo controllers) to work with Linux and ROS.
- Collaboration and Documentation: Working closely with hardware engineers, application teams, and QA testers, and creating technical documentation for system architecture.
Minimum Requirements:
- Education: Masters/PhD in Computer Science, Machine Learning, AI, Robotics, or related field.
- Experience: At least 5 years experience building robotics/Physical AI systems.
- Operating Systems Internals: Strong understanding of operating system concepts, including process management, multi-threading, concurrency, memory management (paging/virtual memory), and interrupt handling.
- Linux Environment: Expert knowledge of Ubuntu Linux, terminal usage, shell scripting (Bash), and networking (TCP/IP).
- ROS Expertise: Deep knowledge of ROS1 & ROS 2, especially ROS 2 and its middleware components (DDS, FastDDS/CycloneDDS).
- Robot Behaviour Orchestration: Experience with Behavior Trees and other robot behavior orchestration frameworks.
- Programming Languages: Deep proficiency in C and C++ is essential for kernel-level development, with Rust increasingly required for modern systems, and Assembly for debugging and performance tuning.
Good to Have:
- GPU Acceleration: Experience with CUDA programming for GPU acceleration.
- Edge AI Deployment: Optimizing models (TensorFlow/PyTorch) to run on low-power OS environments.
- Robotics Core Algorithms: Understanding of Kinematics, Dynamics, SLAM (Simultaneous Localization and Mapping), Kalman Filters, and Path Planning.
- GenAI for Robotics: Experience with implementing LLM based Semantic Reasoning and Task Decomposition for decision making, and/or VLM based Perception/Mapping for world state estimation.
Soft Skills:
- Problem-Solving: Methodical approach to diagnosing and resolving complex, low-level issues.
- Attention to Detail: Meticulous approach, as small errors in OS code can have significant consequences.
- Communication: Ability to explain complex technical concepts clearly to cross-functional teams.
What We Offer:
Opportunity to work on state-of-the-art embodied AI models powering real robots.
Combination of research and deployment - not just writing models, but seeing them act in the physical world.
High-impact work on cutting-edge robotic autonomy and swarm behaviours.
Exposure to both AI and hardware-level execution environments.
