Wireless Network Lab Intern

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Wireless Network Lab Intern Chandler, AZ USA Summary: VIAVI (NASDAQ: VIAV) is a global provider of network test, monitoring and assurance solutions for telecommunications, cloud, enterprises, first responders, military, aerospace, and railway. VIAVI is also a leader in light management technologies for 3D sensing, anti-counterfeiting, consumer electronics, industrial, automotive, government and aerospace applications. We are the people behind the products that help keep the world connected at home, school, work, at play, and everywhere in between. VIAVI employees are passionate about supporting customer success and we welcome people who bring their best every day to the company - to question, to collaborate and to push for solutions that will delight our customers. Duties & Responsibilities: Responsibilities Design and develop AI/ML models tailored to telecom operational use cases, including diagnostics, protocol analysis, and test automation. Build and manage data pipelines - curate, clean, and structure training datasets from diverse telecom sources. Run training experiments on GPU infrastructure and iterate on model performance. Develop evaluation benchmarks and validate model outputs against domain expert assessments. Collaborate with cross-functional engineers (RF, RAN, security, software) to translate domain expertise into model capabilities. Contribute to a research publication documenting methodology and results. Pre-Requisites / Skills / Experience Requirements: Required Qualifications Currently pursuing a Ph.D. (or advanced M.S.) in Electrical Engineering, Computer Science, or a related field. Research focus in machine learning, with hands-on experience in deep learning frameworks (PyTorch or TensorFlow). Experience training and fine-tuning large-scale models. Strong Python proficiency and familiarity with GPU-accelerated workflows. Track

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