Title:  Principal Physical AI Engineer (ROS Architect)

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: Masters (10 – 15 years)/ PhD (3-5 years) of 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.