Engineering Manager - AI for Ran and 6g Wireless Systems

NVIDIA · Semiconductors · Hanoi, Vietnam +1

NVIDIA is seeking an Engineering Manager to lead a team developing AI/ML models for 6G wireless networks. The role involves guiding model development, training, evaluation, and deployment, with a focus on integrating deep learning into signal processing and radio access technologies. Experience with Python, PyTorch/TensorFlow, and leading engineering teams is required.

What you'd actually do

  1. Lead and grow a high-impact engineering team focused primarily on deep learning model development and deployment (ML/DL is the core focus).
  2. Guide the development of deep learning models and architectures (e.g., CNNs, Transformers) for real-world integration and/or deployment.
  3. Collaborate with global teams across architecture, research, and systems to drive proof-of-concepts and production-quality AI components.
  4. Oversee model training/evaluation and integration into simulations and/or testbeds using frameworks such as PyTorch, TensorFlow, and NVIDIA Sionna.
  5. Align project priorities with hardware-software co-design constraints and deployment scenarios on NVIDIA platforms.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • deep learning frameworks
  • machine learning fundamentals
  • deep learning fundamentals
  • training pipelines
  • evaluation
  • model iteration
  • neural network architectures (CNNs, Transformers)
  • technical leadership
  • collaboration
  • communication

Nice to have

  • model compression
  • real-time inference
  • GPU optimization
  • performance tuning for deployment
  • AI for 5G/6G systems
  • AI-for-RAN architecture
  • telecom-grade deployments
  • RIS
  • massive MIMO
  • THz communication
  • research
  • publications
  • open-source contributions
  • wireless communications

What the JD emphasized

  • deep learning model development and deployment
  • real-world integration and/or deployment
  • production-quality AI components
  • model training/evaluation
  • deployment scenarios
  • delivering production-grade ML systems
  • applied research to deployment
  • real-world applications
  • training pipelines, evaluation, and model iteration

Other signals

  • deep learning model development and deployment
  • AI-native wireless networks
  • integration and/or deployment of deep learning models