Computer Networking Internship Jobs in Edison, NJ

Edge and Industrial Network Systems Research Internship
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrive is if our people are thriving. That's why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways.
Siemens Research & Predevelopment (RPD) is the central R&D department of Siemens and thus has a key role to shape the future of our products. RPD acts as a strategic partner to support the executive units of Siemens. In consequence the main research focus is on future technologies for industry, infrastructure, mobility, and healthcare. In this context, we are looking for an Intern that supports our Software Systems and Processes team in Princeton, NJ by researching and developing scalable intelligent edge and networked systems for industrial applications.
Transform the everyday with us!
Are you passionate about ensuring the reliability and robustness of cutting-edge AI systems? We're looking for an innovative intern to join our team and contribute to research and prototyping at the intersection of Industrial Edge, OT/IT integration, and production optimization.
Modern industrial systems increasingly rely on connected edge devices, distributed data pipelines, and real-time software services that bridge operational technology (OT) and information technology (IT). As manufacturers seek faster deployment, better visibility into machine behavior, and more scalable production analytics, the challenge is no longer only collecting data, it is building reliable, secure, and efficient edge and network architectures that can connect machines, move data seamlessly, and enable actionable insights across industrial environments. In this internship, you will research and prototype next-generation approaches for industrial edge connectivity, edge application orchestration, machine data integration, and network-aware production intelligence. You will help advance practical and scalable concepts for real-world industrial software systems where interoperability, reliability, transparency, and operational efficiency matter.
The internship provides a unique opportunity to contribute to innovative industrial applications while being mentored by experienced professionals in an international setting.
This role is preferred to be on-site in Princeton, NJ, for a hands-on and collaborative experience; however, remote candidates will be considered. The position is a full-time role for at least 3 months with the possibility of extension.
Key Responsibilities

• Research, design, and prototype methods for scalable industrial edge and networked software systems, with emphasis on connectivity, interoperability, reliability, performance, and maintainability in shopfloor environments.
• Build and evaluate edge application workflows involving components such as device management, data brokers, industrial data ingestion pipelines, event-driven processing, dashboards, and notification services.
• Investigate architectures and design patterns for distributed edge systems, including secure communication, message-based integration, application deployment, observability, and runtime monitoring across connected industrial assets.
• Prototype solutions in C#, Python and related technologies to support edge analytics, automated reporting, equipment efficiency monitoring, and end-to-end industrial data flows across edge and cloud-connected environments.
• Collaborate with researchers and engineers to define milestones, run experiments, analyze system behavior, and translate research insights into scalable industrial software concepts and product-relevant innovations.
• Document findings, contribute to technical reports or scientific publications, and present results clearly to internal and external technical audiences.

Basic Qualifications

• Currently enrolled in a Master's or PhD program in Computer Science, Electrical Engineering, Computer Engineering, Software Engineering, Networking, Cyber-Physical Systems, Industrial Informatics, or a closely related technical field.
• 3+ years of research or hands-on experience in edge computing, distributed systems, computer networking, industrial IoT, cyber-physical systems, or industrial software systems.
• Programming skills in C#, Python and experience developing software prototypes, system integrations, or data-processing pipelines.
• Understanding of computer networking fundamentals, including TCP/IP, routing, switching, DNS, firewalls, VPNs, network segmentation, and secure communication across distributed systems.
• Hands-on experience with networked systems, industrial communication environments, or edge-to-cloud connectivity, including troubleshooting, performance analysis, and reliable data exchange across heterogeneous devices and services.
• Experience working with data collection, analytics, or monitoring systems in industrial, IoT, edge, or networked environments.
• Hands-on experience with modern software engineering workflows and CI/CD practices, including Git, automated testing, build and release pipelines, and deployment processes for distributed, networked, or edge-based applications.
• Proficient in English, both written and verbal.
• The position requires the person to be in the United States of America and hold a valid work permit in the US for the duration of the internship.

Preferred Skills

• Research or hands-on experience with industrial edge platforms, industrial IoT architectures, edge orchestration, or distributed application deployment.
• Familiarity with industrial connectivity technologies and protocols, such as MQTT or similar OT/IT integration approaches.
• Experience with industrial and enterprise network architectures, including OT/IT integration, edge network design, secure remote access, VLANs, NAT, MQTT-based communication, connectivity, or similar industrial networking technologies.
• Familiarity with network observability and diagnostics tools such as packet capture, protocol analysis, traffic monitoring, latency profiling, or distributed communication debugging in complex edge or industrial environments.
• Experience with containerization and deployment technologies such as Docker, Kubernetes, or cloud-edge deployment workflows.
• Experience with observability, logging, trace-based monitoring, runtime diagnostics, or performance analysis for distributed systems.
• Excellent problem-solving skills, attention to detail, and ability to quickly learn and apply new technologies, tools, and research methods.
• Strong written and verbal communication skills, with the ability to articulate complex technical concepts to research and engineering audiences.

About Siemens
We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resource-efficient factories, resilient supply chains, and smarter buildings and grids, to sustainable transportation as well as advanced healthcare, we create technology with purpose, adding real value for customers. Learn more about Siemens here.
Our Commitment to Equity and Inclusion in our Diverse Global Workforce
We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us.
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You'll Benefit From
Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
The pay range for this position is $32-$47 per hour. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications and premium geographic location.
Equal Employment Opportunity Statement
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