Senior Wireless Machine Learning Engineer, AI-RAN
DeepSig Inc
Job Type
Full-time
- 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
- 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
Vacancy posted 4 days ago
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