Data & AI Engineer Intern: Build Scalable ML Pipelines

Cisco; San Jose (CA); On-site; USD 50,000 - 80,000; Full time

Benefits offered by this job

Medical, dental and vision insurance

401(k) with Cisco match

Paid parental leave

Disability coverage

Life insurance

Job summary

Cisco seeks motivated students for internship opportunities in our Product Organization in San Jose, CA. You’ll build data pipelines and ML workflows using Python, train and deploy models with frameworks like PyTorch or scikit-learn, and orchestrate large-scale data solutions with Spark and Kubernetes.

You’ll contribute to intelligent data pipelines, ensuring performance and reliability as datasets scale, while collaborating with engineers and researchers in a fast-paced AI-driven environment.

Qualifications

• Proficiency in Python and data structures demonstrated via projects
• Exposure to ML frameworks (TensorFlow, PyTorch, or scikit-learn)
• Experience with big data tools (Spark, Hadoop, or Flink)
• Hands-on with deployment/orchestration tools (Docker or Kubernetes)
• Currently enrolled in degree program and plan to continue after internship

Responsibilities

• Build data pipelines and ML workflows powering real-world products
• Develop, train, and deploy models in a scalable environment
• Orchestrate data solutions using Spark and Kubernetes
• Collaborate with teams to ensure performant and reliable AI platforms

Skills

Python

ML frameworks

Spark

Kubernetes

Docker

Data pipelines

Cloud exposure

Education

Undergraduate degree program (Assoc/Bachelor) 2 yrs experience

Master’s degree program 0 yrs experience

Tools

TensorFlow

PyTorch

scikit-learn

Spark

Hadoop

Docker

Kubernetes

Job description

Cisco seeks motivated students for internship opportunities in our Product Organization in San Jose, CA. You’ll build data pipelines and ML workflows using Python, train and deploy models with frameworks like PyTorch or scikit-learn, and orchestrate large-scale data solutions with Spark and Kubernetes.

You’ll contribute to intelligent data pipelines, ensuring performance and reliability as datasets scale, while collaborating with engineers and researchers in a fast-paced AI-driven environment.