AI/Machine Learning Engineering Intern

Key Skills

Aartificial intelligence, Analytical and Problem solving, Machine learning techniques, R Programming, UI design, UX design

Job Description

Qorvo’s Internship Program is designed for college students currently enrolled in an accredited Bachelor’s, Master’s, or PhD program. Qorvo offers real work experience, exposure to upper management, and the opportunity to pursue full time opportunities, as available.

Qorvo’s Internship Program offers:

• Challenging, skill-building assignments
• Mentoring and coaching from industry experts
• Lunch & Learns and other learning opportunities
• Collaborative team-based work environment
• Networking and social events
• Final presentation to business leaders

Qorvo’s AI/Machine Learning Engineering Internships are offered in our High Performance Analog business group. Specific projects and responsibilities will be determined based on the business needs at the time of the internship assignment.

Responsibilities may include:

• Data Collection and Preprocessing: Gather and preprocess RF circuit data, including simulations, measurements, and real-world performance data.
• Feature Engineering: Identify relevant features and parameters for RF circuit analysis and design machine learning models to extract these features from raw data.
• Model Development: Create and train AI/ML models for RF circuit analysis, including predictive modeling, anomaly detection, and optimization algorithms.
• Algorithm Implementation: Implement AI/ML algorithms and integrate them into existing RF circuit analysis tools and workflows.
• Performance Evaluation: Evaluate the performance of AI/ML models and algorithms through extensive testing, validation, and benchmarking against traditional methods.
• Collaboration: Collaborate with RF engineers, software developers, and other cross-functional teams to ensure seamless integration of AI/ML solutions into RF circuit design and analysis processes.
• Documentation: Document your work, including algorithms, methodologies, and results, to facilitate knowledge sharing and future improvements.

Qualifications:

• Currently enrolled in a graduate-level program in AI/ML
• Strong background in AI/ML, including experience with TinyML and embedded applications
• Knowledge of RF circuits and familiarity with simulation tools (e.g., Cadence, ADS, HFSS) is a plus.
• Strong problem-solving skills and the ability to work independently and collaboratively in a team.
• Excellent communication skills and the ability to present findings and results effectively.