6G System Prototype Development Engineer

China Telecom Singapore Innovation Research Institute; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

China Telecom Singapore Innovation Research Institute is looking for a researcher to advance AI-native RAN architectures and 5G networking. The role emphasizes intelligent scheduling, dynamic resource allocation, and adaptive beamforming within next‑gen wireless systems.

You will build and optimize AI/ML models for link adaptation, interference management, and energy efficiency, while creating data pipelines for training and real-time inference.

Qualifications

• PhD or MSc in Electronic/Communication Engineering or Computer Science
• Strong development record in AI-native RAN, wireless system, or 5G networking
• Experience in L1-L3 layer design for 5G systems
• Proficiency in deep learning / RL / FL for RAN optimization
• Experience in AI inference for 5G RAN optimization
• Strong programming skills in Python (PyTorch/TensorFlow), C++ for prototyping
• Demonstrated experience building and validating 5G RAN systems

Responsibilities

• Design AI-native RAN architectures and AI-driven features across PHY–RAN
• Develop and optimize AI/ML models for link adaptation, interference management, user scheduling, energy efficiency
• Build data pipelines for data collection, preprocessing, labeling, and real-time inference
• Validate AI modules within the 5G base station software stack (PHY, MAC, RLC, PDCP)
• Collaborate with hardware teams to deploy on FPGA/ASIC/GPU/DSP
• Contribute to standardization efforts (3GPP, O-RAN) by integrating AI-native RAN features

Skills

AI-native RAN development

5G/wireless systems

L1-L3 layer design

Deep learning / RL / FL

AI inference in RAN

Python (PyTorch/TensorFlow)

C++ programming

5G RAN validation

Education

PhD or MSc in Electronic Engineering, Communication Engineering or Computer Science

Job description

• Contribute to the system architecture design, Algorithm/Model development, System implementation and optimization, Cross-functional cooperation and Test & validation. including:
• Participate in the design of 5G RAN system architecture, with a focus on AI-driven network functionalities (e.g., intelligent scheduling, dynamic resource allocation, adaptive beamforming). Define interfaces and data flows between AI-native RAN modules and traditional base station/core network functions. Identify AI embedding points across L1–L3 layers to enhance network performance.
• Develop, train, and optimize AI/ML models for wireless link adaptation, interference management, user scheduling, and energy efficiency optimization. Build data pipelines for collection, preprocessing, and labeling to support AI model training and real-time inference. Explore lightweight AI approaches such as federated learning and edge inference for RAN scenarios.
• Implement and validate AI modules within the 5G base station software stack (PHY, MAC, RLC, PDCP, etc.). Conduct simulation and field testing to evaluate throughput, latency, spectrum efficiency, and energy efficiency improvements. Optimize computation and memory overhead to enable efficient AI execution on edge devices (DU, RU, gNodeB).
• Work closely with hardware teams to ensure deployability of AI-native RAN solutions on FPGA, ASIC, GPU, or DSP platforms. Collaborate with operations teams to support AI-driven OAM (Operations, Administration, and Maintenance), such as auto-tuning and fault detection. Contribute to standardization efforts (3GPP, O-RAN Alliance) by integrating AI-native RAN features into evolving standards.
• Develop and maintain AI-native RAN performance evaluation frameworks, including simulation and lab testing platforms. Create test cases and validation reports to ensure compliance with 5G standards and operator requirements. Analyze field data to continuously refine AI models and optimize system parameters.

Requirements:

• PhD or MS degree in Electronic Engineering, Communication Engineering or Computer Science (or a closely related discipline)
• Strong development record in AI-native RAN, wireless system, or 5G networking
• Experience in L1-L3 layer design for effective 5G systems
• Proficiency in deep learning (CNN, RNN, GNN), reinforcement learning (RL), or federated learning (FL) applied to RAN optimization
• Experience in AI inference for 5G RAN optimization
• Strong programming skills in Python (PyTorch/TensorFlow), C++, for system prototyping
• Demonstrated experience building and validating 5G RAN systems