Summary
What you’ll impact
Our organization is seeking an experienced Technical Lead to architect and drive development of an AI‑native Radio Access Network, designing and validating novel AI/ML components for next‑generation wireless communications. The role involves applied AI research, high‑fidelity simulation, prototyping, data engineering, and contributing to innovation and standardization efforts.
Responsibilities
What you'll do
- Applied AI Research: Design and train modern deep learning models (Transformers, Vision architectures, etc.) to solve complex physical layer problems, including channel estimation, MIMO detection, and beam management
- Simulation & Validation: Build high-fidelity link-level simulations using NVIDIA Sionna and ray-tracing to train, test, and benchmark AI models against legacy 5G baselines
- Prototyping & Deployment: Transition research models into deployable "dApps" for the Distributed Unit (DU), optimizing inference for latency and compute efficiency on NVIDIA GPUs
- New Capabilities: Explore emerging AI-RAN frontiers such as Integrated Sensing and Communications (ISAC), neural scheduling, and channel digital twins
- Innovation & IPR: Drive technical innovation by authoring invention disclosures, filing patents, and generating technical reports to support our standardization team in 3GPP and O-RAN Alliance contributions
- Data Engineering: Architect data pipelines for generating synthetic training datasets and developing "Sim-to-Real" transfer techniques to ensure robust performance in real-world networks
Requirements
What you’ll bring
- Education: Ph.D. or Master’s in Computer Science, Electrical Engineering, or Applied Mathematics with a focus on Deep Learning and/or Communications Systems
- AI/ML Expertise: 3+ years of experience designing and training deep neural networks from scratch. Strong grasp of modern architectures and optimization techniques
- Applied Signal Processing: Experience applying machine learning to real-time time-series data, signal processing, or physics-based problems (Audio, RF, or similar domains)
- Research to Code: Proven ability to read academic papers and implement their methods in robust Python code
- Simulation Skills: Experience with differentiable simulation or digital twins (e.g., Sionna, JAX-based physics sims)
- Wireless Knowledge: Understanding of wireless fundamentals (OFDM, MIMO, IQ data) is highly helpful, though we prioritize strong ML intuition over pure communication theory
- Performance Optimization: Experience with model quantization (FP16/INT8), pruning, or using TensorRT for real-time inference
- Standardization Support: Experience writing technical whitepapers or supporting patent filings in a research environment
- C++ Integration: Ability to write C++ bindings or integrate Python models into C++, SIMD, and Cuda production pipelines