Internship – Artificial Intelligence

APM Terminals•Los Angeles, CA

2d•Onsite

About The Position

The AI & Machine Intelligence Intern supports the Operations Intelligence and Optimization team by assisting in the development and implementation of artificial intelligence initiatives focused on computer vision, equipment performance analytics, and predictive maintenance within an automated terminal environment. Working under the guidance of the Data Engineer, Operations Intelligence and Optimization (within the APMT Pier 400 Data and Optimization Department), the intern will contribute to data science and software development projects that leverage machine learning, automation data, and advanced analytics to improve operational efficiency, equipment reliability, and decision-making. This role provides hands-on experience with real-world industrial AI applications, including automated equipment monitoring, image/video analysis, and performance prediction modeling.

Requirements

Currently pursuing a Bachelor’s or Master’s degree in: Computer Science, Data Science, Artificial Intelligence, Robotics, Electrical Engineering, Industrial Engineering, Or a related technical field
Experience with Python and common data science libraries (NumPy, Pandas, Scikit-learn, etc.)
Familiarity with machine learning and/or deep learning frameworks (PyTorch, TensorFlow, or similar)
Exposure to computer vision concepts or tools (OpenCV, image classification, object detection, etc.)
Basic SQL and data analysis experience
Familiarity with Git or version control practices
Experience working with APIs and data ingestion workflows
Strong analytical and problem-solving skills
Demonstrated curiosity and willingness to learn new technologies
Ability to communicate technical findings clearly
Strong organizational and documentation skills
Ability to work collaboratively within cross-functional teams
Self-driven with strong attention to detail

Nice To Haves

Exposure to cloud platforms (Fabric preferred), containerization, or deployment workflows is a plus
Coursework or project experience in AI, machine learning, or computer vision
Experience working with time-series or sensor data
Familiarity with industrial automation, robotics, or IoT data environments is a plus
Interest in logistics, transportation, or automated equipment systems

Responsibilities

Assist in designing, developing, and testing machine learning models focused on: Computer vision applications for automated equipment monitoring, Equipment health monitoring and predictive maintenance, Operational performance classification and anomaly detection.
Support model training, validation, and evaluation using structured and unstructured operational data.
Assist with data labeling, preprocessing, and feature engineering for machine learning workflows.
Contribute to experimentation and prototyping of AI-based proof-of-concept solutions.
Support ingestion and preparation of video, image, and sensor datasets from automated terminal equipment.
Assist in developing pipelines for processing machine-generated data from PLC, MQTT, and other industrial data sources.
Participate in building and testing computer vision solutions utilizing modern frameworks (e.g., OpenCV, PyTorch, TensorFlow, or similar).
Support development of visual analytics and monitoring dashboards showcasing AI insights.
Assist in building visualizations and performance reporting tools using business intelligence platforms (PowerBI).
Assist in developing data pipelines and workflows supporting AI and analytics initiatives.
Support database queries, data transformation, and exploratory data analysis using SQL and Python.
Help document data sources, data models, and AI experimentation results.
Work alongside optimization, automation, data, and operational teams to understand business use cases and equipment behavior.
Participate in brainstorming and research initiatives for emerging AI technologies applicable to terminal operations.
Present project updates, findings, and recommendations to technical and operational stakeholders.
Support knowledge sharing and documentation for AI initiative scalability.