AI R&D Engineer Co-op
Nokia
Software Engineering, Data Science
United States
Posted on Dec 16, 2025
Number of Position(s): 1
Duration: 4 Months
Date: May 11 - August 21, 2026
Location: Onsite
Education Recommendations:
Currently a candidate for a Master’s or PhD degree in Computer Science or Engineering, Mathematics, or a related field with an accredited school in the US.
Advancing connectivity to secure a brighter world.
Nokia is a global leader in connectivity for the AI era. With expertise across fixed, mobile and transport networks, powered by the innovation of Nokia Bell Labs, we’re advancing connectivity to secure a brighter world.
About the Business Group
In Cloud and Network Services, as Nokia's growth engine, we create value for communication service providers and enterprise customers by leading the transition to cloud-native software and as-a-service delivery models. Our inclusive team of dreamers, doers and disruptors push the limits from impossible to possible.
Our recruitment process
We act inclusively and respect the uniqueness of people. Our employment decisions are made regardless of race, color, national or ethnic origin, religion, gender, sexual orientation, gender identity or expression, age, marital status, disability, protected veteran status or other characteristics protected by law. We are committed to a culture of inclusion built upon our core value of respect.
If you’re interested in this role but don’t meet every listed requirement, we still encourage you to apply. Unique backgrounds, perspectives, and experiences enrich our teams, and you may be just the right candidate for this or another opportunity.
The length of the recruitment process may vary depending on the specific role's requirements. We strive to ensure a smooth and inclusive experience for all candidates. Discover more about the recruitment process at Nokia.
Some of our benefits for students in the US:
- Flexible and hybrid working schemes to balance study, work, and life
- Professional development events and networking opportunities
- Well-being programs, including Personal Support Service 24/7 - a confidential support channel open to all Nokia employees and their families in challenging situations
- Opportunities to join Nokia Employee Resource Groups (NERGs) and build connections across the organization
- Employee Growth Solutions, mentorship programs, and coaching support for your career development
- A learning environment that fosters both personal growth and professional development – for your role and beyond
Disclaimer for US/Canada
Nokia maintains broad annual base salary ranges for its roles in order to account for variations in knowledge, skills, experience and market conditions, and with consideration to internal peer equity. Check the salary ranges in the job info section for this role.
All North America job posts will post for a minimum of 3 calendar days and up to 180 days or until candidate/s identified.
As an AI R&D Engineer student at Nokia, you will be responsible for engaging R&D of cutting-edge AI solutions that can effectively leverage Generative AI, Agents, Deep Learning, and Machine Learning to power a wide range of products & platforms – from analytics to security to telecom core capabilities. Nokia relies on innovative AI research and applications that you will help us build.
- Experience with:
- Machine learning, optimization algorithms, and deep-learning techniques.
- Machine learning frameworks (e.g., TensorFlow, PyTorch).
- Search engines and vector databases, along with their underlying algorithms.
- Big data frameworks and technologies such as Spark, Kafka, and Cassandra.
- Excellent communication skills and is a team player.
As part of our team, you will:
- Design, develop, and deploy advanced AI/ML models and algorithms to analyze and interpret complex data.
- Design and implement machine learning models to improve a wide range of applications, including search, forecasting, text mining, and more.
- Develop and implement agentic-based systems for a wide range of applications, including anomaly detection, root-cause analysis, and more.
- Optimize existing machine learning models and pipelines for performance, scalability, and resource efficiency.