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Senior AI/ML Algorithms Engineer
PresencialTiempo completoAI & Machine LearningTelecommunications
Join a telecommunications technology company developing energy-efficient Open RAN products. You will research, design, optimize, and integrate machine-learning algorithms for wireless physical-layer processing in 5G and future cellular systems.
Responsabilidades
- Research wireless communication algorithms while balancing performance, implementation cost, real-time constraints, and delivery timelines.
- Design and train neural-network models for PHY tasks such as channel estimation, signal detection, beamforming, and decoding.
- Develop algorithms from literature review and simulation through customer release, including prototyping, modeling, specification writing, implementation support, and end-to-end integration.
- Use Python, PyTorch, TensorFlow, and MATLAB for algorithm and model development.
- Benchmark machine-learning solutions against traditional digital signal-processing approaches using accuracy, latency, computational cost, and system performance measures.
- Target real-time inference on embedded platforms for 5G and future wireless products.
Requisitos
- At least 3 years of hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow and neural-network architectures including CNNs, RNNs, transformers, or autoencoders.
- Experience with model optimization for real-time deployment, including quantization, pruning, knowledge distillation, or hardware-aware neural architecture search.
- Strong mathematical and analytical skills with the ability to solve problems independently.
- Master’s or doctoral degree in electrical engineering, preferably focused on communication theory and systems, signal processing, or machine learning.
- Strong communication skills and the ability to work effectively in a global, multi-site team.
Se valora
- Experience applying machine learning or deep learning to physical-layer problems such as channel estimation, MIMO detection, CSI feedback, learned codebooks, or end-to-end learned communications.
- Experience developing PHY algorithms for wireless modems.
- Understanding of LTE and NR cellular standards.
- Experience with ONNX Runtime, TensorRT, or comparable inference engines.