Roles & Responsibilities
We are seeking a Principal Robotics Systems Engineer to lead the development of communication architectures and embedded hardware integration for multi-agent robotic systems at AI.DA, Strategic Technology Centre (STC)'s Next-gen Edge AI & Robotics Lab (NEAR). This role is central to bridging the gap between high-level AI algorithms and physical platforms (UGVs, aerial systems, and mobile manipulators). You will be responsible for ensuring low-latency data exchange, deterministic embedded performance, and robust electrical stability across diverse robotic fleets, enabling coordinated autonomous operations in research and field environments.
Distributed Communications & Networking
High-Bandwidth Mesh Networking: Design and implement wireless communication layers using modern middleware such as Zenoh or CycloneDDS, optimized for multi-agent data exchange in bandwidth-constrained or unstable environments.
Deterministic Connectivity: Develop multi-link communication strategies (e.g., Wi-Fi 7, Private 5G, and Long-range RF) to maintain persistent telemetry and command links for remote and decentralized operations.
Spatial & Temporal Synchronization: Integrate and calibrate hardware for precise time-synchronization and relative localization (e.g., Ultra-Wideband (UWB) or RTK-GNSS) to enable tight coordination between multiple agents.
Network Performance Tuning: Monitor and optimize data throughput and latency for high-bandwidth perception streams required by Vision-Language-Action (VLA) models.
Embedded Systems & Firmware Engineering
Real-time Firmware Development: Architect and write C/C firmware for microcontrollers (e.g., STM32, ESP32-S3) to manage low-latency control loops and sensor data acquisition.
Middleware & OS Optimization: Configure and optimize ROS 2 nodes and embedded Linux environment to ensure deterministic execution of critical autonomy tasks.
Edge AI Infrastructure: Implement and manage the hardware abstraction layer (HAL) and drivers required to deploy transformer-based or deep learning models on edge compute modules (e.g., NVIDIA Jetson Orin).
Deployment Automation: Develop tools for fleet-wide firmware updates, remote hardware diagnostics, and secure system configuration management.
Electrical Integration & System Orchestration
Modular Power Architecture: Design and implement robust power distribution systems using smart regulators and battery management systems (BMS) to support high-compute edge devices and high-torque actuators.
Component Integration: Select and interface state-of-the-art COTS sensors (e.g., 4D Imaging Radar, Solid-state LiDAR, Event Cameras), ensuring optimal placement and EMI/EMC mitigation.
Interface Development: Fabricate custom wiring harnesses and modular interface solutions to maintain signal integrity across heterogeneous hardware components.
Thermal Management: Integrate active and passive cooling solutions to maintain operational stability during intensive on-board Edge AI processing.
Tell employers what skills you have
Signal Integrity
Embedded Linux
Autonomy
Localization
Wiring
Architect
Throughput
Wireless
Power Distribution
Firmware
Telemetry
Microcontrollers
Architecture Design
Sensors
Performance Tuning
Robotics
