ML Engineer - Internship

About Alta Ares

Alta Ares is an air defense Neoprime. We build AI-guided interceptors to counter drones and cruise missiles, along with the software platform that powers them.

Our customers are NATO-aligned militaries and defense institutions, and our systems are regularly deployed during live exercises and operational demonstrations across Europe, the Middle East, and Asia.

Founded in 2024, we have grown to 90 people, raised over $60M, and increased revenue 40× this year. We are now entering a new phase of international expansion, industrial scaling, fundraising, and product development.

Role & Mission

We are looking for a motivated Machine Learning Engineer Intern to join our Data & AI team and contribute to the development of our end-to-end ML pipelines.

You will help improve how we train, evaluate and deploy computer vision models, working on internal AI infrastructure and model lifecycle management.

This internship is ideal for someone interested in applied machine learning, computer vision and MLOps, who wants to gain hands-on experience building reliable AI systems for real-world defence applications.

Key Responsibilities

Model training: Improve and optimise training pipelines for computer vision tasks, including object detection, segmentation and tracking.

Model evaluation: Develop evaluation frameworks using standard metrics such as mAP, IoU, precision and recall, and automate experiment tracking.

Data management: Build reproducible workflows for dataset versioning, augmentation and preprocessing of large-scale image and video datasets.

Model deployment: Contribute to deployment workflows for optimised AI models running on edge devices and embedded systems.

Monitoring and reliability: Implement tools to monitor deployed models and assess their robustness and performance.

Documentation and collaboration: Work closely with the Data & AI team to document best practices and help build scalable ML infrastructure.

Candidate Profile

Strong foundations in Python, data science and machine learning.

Knowledge of deep learning frameworks such as PyTorch or TensorFlow.

Ability to analyse, visualise and preprocess large-scale image and video datasets.

Familiarity with experiment tracking tools such as MLflow or Weights & Biases, and/or data versioning tools such as DVC.

Understanding of Docker, Git and cloud platforms such as AWS or GCP.

Ability to work autonomously in a research and prototyping environment.

Strong collaboration skills and an interest in working in a fast-paced, mission-driven environment.