Job Description
Key Responsibilities
- Assist in designing and optimizing AI/ML data models for wireless cellular networks.
- Preprocess and analyze large-scale wireless data (e.g., signal strength, QoS metrics, network logs).
- Implement feature engineering techniques to improve model performance.
- Integrate AI models with cellular data pipelines and network simulation environments.
- Collaborate with R&D teams to validate models using real-world or simulated wireless datasets.
- Document workflows, model performance, and integration steps.
Qualifications
- Programming: Proficiency in Python (preferred), with experience in libraries like TensorFlow, PyTorch, or Scikit-learn.
- Data Handling: Strong knowledge of data preprocessing, normalization, and handling large datasets.
- AI/ML Fundamentals: Understanding of supervised and unsupervised learning, model optimization, and evaluation metrics.
- Wireless Basics: Familiarity with cellular technologies (4G/5G concepts, signal processing, network KPIs).
- Tools: Experience with Jupyter, Git, and basic cloud platforms (AWS, Azure, or GCP).
- Math & Stats: Solid foundation in linear algebra, probability, and optimization techniques.
- Coursework or projects in wireless communication systems or network optimization.
- Exposure to big data frameworks (Spark, Hadoop) or real-time data streaming.
- Knowledge of edge AI or AI for telecom use cases.
- Strong analytical and problem-solving skills.
- Ability to work independently and in a collaborative team environment.
- Good communication and documentation skills.
Candidates who wish to be considered must be enrolled in a accredited college/university as of September 2026. Applicants who have graduated before September 2026 will not be considered unless they are entering/applying to a MS or PHD program after graduating.
