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Software Dev AI​/ML Coop

Job in Naperville, Will County, Illinois, 60564, USA
Listing for: Nokia
Full Time position
Listed on 2026-01-12
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Location: Naperville

Position: Software Dev AI/ML Co-op

Number of Position(s): 1

Duration: 3 Months+

Date: May 2026 to August 2026

Location: Naperville, IL - Hybrid

The team you'll be part of

You will be part of the Cloud Mobility Manager (CMM) within Nokia's CNS Division. The CMM delivers a converged packet core solution that addresses 5G (AMF), 4G LTE (MME), and even 2G/3G in a fully cloud-native architecture. Invent 5G with us, and be part of a diverse, multi-site world-class R&D team!

Education Recommendations

Currently a candidate pursuing a bachelors or masters degree in Data Science, Computer Science, Computer Engineering, or a related field with an accredited school in the US.

Qualifications
  • Pursuing a Data Science degree or degree in a similar field with some experience in data classification models, time series, and large language models.
  • Python proficiency, C++/C language knowledge
  • Experience with the Exploratory Data Analysis (EDA) process – end to end, including projects utilizing the EDA
  • Jupyter Notebooks and knowledge of common AI prototyping tools/libraries.
It would be nice if you also had:
  • Networking IP Knowledge (IPv6/IPv4), familiarity with Linux, Git, and AI tools: MLFlow, Air Flow, and Scikit Learn.
Responsibilities

Work on technologies to improve development & prototyping in Virtualization/Cloud Computing including technologies like:

  • Container Technology/Kubernetes platforms
  • Artificial Intelligence and Machine Learning pipelines

Contribute to a project in ONE of the following areas:

  • Data analytics/Machine Learning, including Exploratory Data Analysis
  • Coding in C++/C and Python which will be used in the implementation of models for data pipeline (training, serving, testing)
  • LSTM models, Embedding Models, Large Language Models (LLM) – encoders/decoders
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