O-RAN AI/ML Engineer (PhD is required)

We are seeking a talented and motivated Wireless Research Engineer with strong software and AI/ML capabilities to join our advanced networking team. Instead of traditional network deployment, this role sits at the cutting edge of Next-Gen cellular architecture: Open RAN (O-RAN). In this role, you will apply your foundational knowledge of wireless communications to design, simulate, and develop intelligent solutions for Near-Real-Time (Near-RT) and Non-Real-Time (Non-RT) RAN Intelligent Controllers (RIC). You will leverage your coding skills in Python, alongside machine learning frameworks, to build the xApps and rApps that drive automation and optimize 5G network performance. This is the perfect launchpad for a telecom graduate eager to build software-driven, intelligent wireless infrastructure.

Key Responsibilities Wireless Modeling & Simulation:

Collaborate with senior engineers on system-level wireless simulations, network modeling, and cellular prototype testing. App Development (xApps/rApps): Develop, test, and integrate intelligent xApps (Near-RT RIC) and rApps (Non-RT RIC) to control radio resources using Python . RAN Optimization via

AI/ML:

Apply machine learning frameworks to solve telecom-specific challenges such as radio resource management (RRM), beamforming, traffic steering, and interference mitigation.

Telecom Data Analytics:

Process and analyze large streams of live RAN telemetry and network KPI data using SQL database .

Standards Alignment:

Study and implement O-RAN Alliance specifications to ensure prototype solutions align with evolving industry standards.

Required Skills & Qualifications Education:

PhD in Electrical Engineering, Telecommunications Engineering , or a closely related field with a heavy focus on wireless systems.

Wireless Deep Dive:

A rock-solid foundational understanding of Wireless Communications , cellular network architecture (4G/5G), and radio frequency (RF) or protocol layer concepts.

O-RAN & RIC

Exposure:

Strong academic familiarity or project work involving O-RAN architecture , RIC platforms, and split-architecture concepts.

Programming & Software:

High proficiency in Python , with the ability to write clean, structured code for network applications.

AI/ML Application:

Hands-on experience (via academic research, capstone projects, or internships) applying ML libraries like TensorFlow or PyTorch to solve engineering or signal processing problems.

Data Basics:

Familiarity with managing data via SQL and exposure to containerized environments (Docker/Kubernetes).