PhD Data & AI Software Engineer - Remote Options

Cisco Systems, Inc; Chicago (IL); Remote; USD 44,000 - 185,000; Full time

Benefits offered by this job

Benefits package

Annual bonus

Volunteer time off

Job summary

Cisco Systems, Inc. offers opportunities for PhD students to contribute to data engineering and AI-driven platforms in the United States.

This role supports building scalable data pipelines, analytics platforms, and ML infrastructure, with collaboration across teams in S&TO, Supply Chain, and Engineering. The internship involves developing scalable data solutions, leveraging TensorFlow, PyTorch, and Spark-based ecosystems, and deploying workloads with Docker and Kubernetes.

Qualifications

• Currently enrolled in a PhD program.
• Qualifying education should be in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Expected to continue enrollment in degree program following completion of the internship.
• Demonstrated proficiency in Python and fundamental data structures.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Familiarity with distributed data processing technologies, including Apache Spark, Hadoop (MapReduce), or Apache Flink.
• Experience with containerization and orchestration tools, such as Docker and Kubernetes, for deploying data and machine learning workloads.

Responsibilities

• Develop scalable data pipelines and analytics platforms.
• Build infrastructure to support machine learning initiatives.
• Collaborate with Cisco business groups across AI initiatives.
• Contribute to the growth of data and intelligent systems within teams.

Skills

Python

Data pipelines

Education

PhD in CS/AI/ML

Tools

TensorFlow

PyTorch

scikit-learn

Apache Spark

Hadoop

Kubernetes

Docker

Job description

Cisco Systems, Inc. offers opportunities for PhD students to contribute to data engineering and AI-driven platforms in the United States.

This role supports building scalable data pipelines, analytics platforms, and ML infrastructure, with collaboration across teams in S&TO, Supply Chain, and Engineering. The internship involves developing scalable data solutions, leveraging TensorFlow, PyTorch, and Spark-based ecosystems, and deploying workloads with Docker and Kubernetes.