Senior Systems Engineer - Wireless ML

Job description

**Job Area:** Engineering Group, Engineering Group > Systems Engineering We are looking for an individual who is passionate about system design and can work closely with data scientists, HW, SW, and test teams to deliver end-to-end solutions. The role requires the ability to **build production-grade deployment systems that bridge the gap between data science and lifecycle management of machine learning models for the wireless domain.** You will conceptualize new solutions to operationalize and scale ML models for wireless networks/cellular chipsets and standards; propose implementation details; evaluate and simulate performance benefits; and analyze/diagnose system-level issues while collaborating across functional teams to meet or exceed system-level requirements and standards. **Minimum Qualifications:** - Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience. - OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience. - OR PhD in Engineering, Information Systems, Computer Science, or related field. **Minimum Qualifications (continued):** - Masters degree in Engineering, Computer Science, Information Systems, Data Science, or related field. - 2+ years of experience in systems engineering, software development, data engineering, or ML engineering (incl. taking models from prototype to deployment). - Strong hands-on expertise applying ML to wireless/communications problems (e.g., signal processing, channel/PHY/MAC modelling, link/system-level simulation, and performance evaluation) using frameworks such as PyTorch or TensorFlow; familiarity with LLM concepts is a plus. - Proficiency in Python and at least one systems programming language (C/C++/Java), with ability to build reliable, testable, production-quality code. **Preferred Qualifications:** - 2+ years industry experience (or strong academic projects) applying ML to wireless/communications (signal processing) and one or more domains such as PHY/MAC/RRM, channel modelling, link-/system-level simulation, and performance trade-off analysis. - Strong fundamentals in probability, statistics, and linear algebra underpinning modern ML/DL for communications. - Strong analytical skills across the ML lifecycle: experiment design, dataset curation, metrics definition, model development/optimization (feature engineering, hyperparameter tuning), and evaluation of wireless ML solutions (accuracy, latency/complexity, robustness, and implementation cost). - Strong hands-on with data structures/algorithms and performance-oriented system design for large-scale data processing, training, and inference. - Strong software engineering for ML systems: Python plus C/C++/Java, clean APIs, testing, and maintainable code. - Forward-looking mindset and awareness of emerging ML techniques relevant to wireless (e.g., graph neural networks and reinforcement learning). - Plus: experience using GenAI/agentic approaches to accelerate wireless engineering workflows (simulation/test automation, log/telemetry analysis, and spec/document retrieval; RAG and tool/function calling). - Plus: experience with cloud platforms (AWS/Azure/GCP), containerization/orchestration (Docker/Kubernetes), and scalable model serving/inference. - Passion for learning and intellectual curiosity.