Opportunity brief
About the Role
PSYC Aerospace & Defence Industries is looking for an AI/ML & Test Engineering Intern to work alongside our engineering team on the development, testing, and validation of AI-driven software and autonomous systems.
This is a hands-on engineering role. You will assist with AI/ML experimentation, dataset preparation, model evaluation, software testing, test automation, debugging, and technical documentation.
The internship is suitable for students who want practical exposure to how AI/ML systems move from development to testing and validation in real engineering applications.
Responsibilities
- Assist in developing and testing AI/ML and computer vision models.
- Prepare, clean, organize, annotate, and validate datasets.
- Run model inference and evaluate performance using appropriate metrics.
- Compare model versions and document performance changes.
- Assist with testing AI perception and autonomy software.
- Develop and execute functional, integration, regression, and system-level test cases.
- Create Python scripts for test automation, data processing, and analysis.
- Test APIs, dashboards, software modules, and data pipelines.
- Identify, reproduce, document, and track software/model issues.
- Analyze logs, telemetry, model outputs, and test results.
- Maintain structured test reports and engineering documentation.
- Assist engineers during simulation, hardware-in-the-loop, and field-testing activities where applicable.
- Work with Git/GitHub for version control and collaborative development.
Required Skills
- Good understanding of Python.
- Basic understanding of Machine Learning and Deep Learning.
- Familiarity with computer vision concepts.
- Understanding of model training, validation, testing, and inference.
- Basic knowledge of NumPy, Pandas, or similar Python libraries.
- Ability to debug code and systematically identify problems.
- Basic understanding of Git/GitHub.
- Good analytical and problem-solving skills.
- Ability to clearly document experiments, bugs, and test results.
Good to Have
Experience with any of the following is an advantage:
- PyTorch or TensorFlow
- OpenCV
- YOLO or other object-detection models
- Dataset annotation tools
- REST APIs and Postman
- pytest or other automated testing frameworks
- Linux/Ubuntu
- Docker
- SQL/databases
- Simulation environments
- Robotics, drones, autonomous systems, or sensor data
- MAVLink / ROS / ROS 2
You do not need to know everything listed above. We are primarily looking for candidates with strong fundamentals, curiosity, and the ability to learn and execute independently.
What You Will Learn
During the internship, you will gain practical exposure to:
- Real-world AI/ML development workflows
- Computer vision and perception systems
- Dataset preparation and model evaluation
- AI model benchmarking
- Software and system testing methodologies
- Test automation
- Debugging and failure analysis
- Engineering documentation
- Git-based collaborative development
- AI and autonomy applications for aerospace and defence systems
Who Can Apply
Students or recent graduates from:
- Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electronics, Robotics, Mechatronics, or related engineering disciplines.
- Candidates with personal projects, GitHub repositories, AI/ML projects, robotics projects, or relevant technical experience are encouraged to apply.
What We Look For
- We value demonstrated ability over certificates.
- Applicants who can show something they have actually built, tested, trained, automated, or contributed to will be preferred.
- Please include your GitHub profile, portfolio, or relevant project links when applying.
Internship Outcome
High-performing interns may receive opportunities to take on greater engineering responsibilities and may be considered for future paid or full-time opportunities based on performance, company requirements, and available positions.
Build. Test. Break. Improve. Repeat.