The company is looking to solve real-world scenarios using AI and machine learning under the guidance of experienced mentors.
Requirements
AI/ML fundamentals and algorithms: learning strategies, feature engineering, generalized and specialized models
Full-stack programming: Python, version control, dashboard, scripting, AI/agentic frameworks
DevOps for AI/ML lifecycle: Kubernetes, continuous deployment, and training
Learning architectures, ML model optimization, model drift detection, lifetime learning, knowledge graphs, or world models
Developing solutions using single or multi-modal data (e.g., vision, text, RF) combined with machine learning to solve interesting problems.
Wireless or wireline connectivity fundamentals, such as communication theory, networking, or interconnect fundamentals.
The ability to interpret and communicate key underlying ideas, concepts, and associated problems in complex research papers and system reports.
Responsibilities
Apply research ideas to real-world scenarios under the guidance of experienced mentors.
Conduct research on novel and existing AI paradigms to address defined problem statements.
Design and develop high-quality software frameworks and tools for end-to-end AI lifecycles.
Manage data collection, calibration, and model development for reliable, scalable performance.
Implement, test, and optimize AI models or agentic workloads for real-time applications.
Communicate results and insights through reports, presentations, papers, or patents.
Collaborate across teams to ensure solutions align with deployment, cost, and performance goals.
Other
The candidate must be enrolled in the second year of a master’s program or in a doctoral program in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related field at an accredited college or university within the United States.
Willingness to contribute with creative, out-of-the-box solutions, to problems arising in a dynamic environment
