Senior Deep Learning Engineer - AI for Wireless Systems

NVIDIA · Semiconductors · Hanoi, Vietnam +1

NVIDIA is seeking a Senior Deep Learning Engineer to develop AI-native wireless networks, integrating deep learning into signal processing and radio access technologies. The role involves designing, prototyping, implementing, training, and optimizing deep learning models for real-time inference and deployment on GPU platforms, collaborating with researchers and system engineers.

What you'd actually do

  1. Design and prototype deep learning models for deployment in RAN-relevant applications.
  2. Work with simulation tools and real-world datasets to build models that generalize across diverse scenarios.
  3. Implement, train, and validate neural networks (e.g., CNNs, Transformers, GNNs) using PyTorch or TensorFlow.
  4. Collaborate with researchers and system engineers to integrate models into full-stack systems and pipelines (RAN integration is a plus).
  5. Optimize model performance for real-time inference and hardware acceleration.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • Deep learning frameworks
  • Neural network architectures (CNNs, Transformers)
  • Training pipelines
  • Evaluation methodology
  • Time-series/sequence/signal-like tasks model training/optimization/deployment

Nice to have

  • MS or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field
  • Wireless/signal processing experience
  • GNNs
  • RAN integration
  • Model compression
  • Real-time inference
  • GPU optimization
  • Performance tuning
  • CUDA
  • Triton
  • Real-time inference pipelines
  • AI for 5G/6G systems
  • AI-for-RAN architecture
  • Telecom-grade deployments
  • Research publications
  • Open-source ML or wireless projects

What the JD emphasized

  • 12+ years of experience in AI/ML and deep learning
  • Strong experience in training, optimizing and deploying deep learning models for time-series, sequence, or signal-like tasks.
  • Solid understanding of modern neural architectures (e.g., CNNs, Transformers; GNNs a plus), training pipelines, and evaluation methodology.
  • Strong track record of delivering production-grade ML models, including benchmarking, robustness work, and deployment readiness.

Other signals

  • AI-native wireless networks
  • deep learning at their core
  • reimagining how wireless systems are designed, trained, and deployed