Principal Robotics Systems Engineer (Embedded, Communications & Electrical)- 2 Years Contract

Title: Principal Robotics Systems Engineer (Embedded, Communications & Electrical)- 2 Years Contract Job ID: 20714 Location: Aero - 600 West Camp Road, SG Description Principal Robotics Systems Engineer (Embedded, Communications & Electrical) Job Overview We are seeking a Principal Robotics Systems Engineer to lead the development of communication architectures and embedded hardware integration for multi-agent robotic , 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.

Required Qualifications Education: Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or Robotics. Experience: 3+ years in a robotics R&D or systems integration environment. Software Mastery: Strong proficiency in C++17/20 and Python 3.x within a Linux/Unix environment. Networking Knowledge: Deep understanding of TCP/IP, UDP, Multicast, and wireless network optimization for mobile agents. Hands-on Skills: Expert level in system-level debugging, custom cabling, and the use of diagnostic tools (e.g., Oscilloscopes, Logic Analyzers, Wireshark).

Preferred Qualifications PhD in a related field with a focus on Distributed Systems or Networked Robotics. Next-Gen Middleware: Hands-on experience with Zenoh for edge-to-edge or edge-to-cloud communication. Edge AI Deployment: Experience setting up hardware environments for deploying large-scale Vision-Language-Action (VLA) models on resource-constrained devices. Rapid Prototyping: Proficiency in 3D CAD (Fusion 360 or SolidWorks) for rapid mechanical packaging and custom sensor mounting solutions.