Description

You design and prototype deep learning models for wireless signal processing tasks, including channel estimation, beam alignment, and scheduling.

This role is hybrid.

Responsibilities

• Design and prototype deep learning models for wireless signal processing tasks.
• Implement, train, and validate neural networks (CNNs, Transformers, GNNs) using PyTorch or TensorFlow.
• Collaborate with researchers and system engineers to integrate models into full-stack RAN.
• Optimize model performance for real-time inference and hardware acceleration.
• Contribute to model evaluation, benchmarking, and deployment readiness on GPU platforms.

Required Skills

• 12+ years of experience in wireless communications, signal processing, or AI/ML.
• Deep understanding of communication systems (MIMO, OFDM, fading channels) and DSP fundamentals.
• Strong experience training and deploying deep learning models for time-series or signal-based tasks.
• Proficiency in Python.
• Experience with PyTorch or TensorFlow.
• Familiarity with MATLAB, GNU Radio, or NVIDIA Sionna for wireless simulation.

Preferred Skills

• Experience with AI for 5G/6G systems or AI-for-RAN architecture.
• Exposure to CUDA, Triton, or real-time inference pipelines.

Key Skills

Education

Any Graduate