AI/Computer Vision Intern - Onboard Detection & Tracking
About Us
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
About the Role
As an AI/Computer Vision Intern, you will join the Perception team to solve one of our most critical challenges: enabling UAVs to "see" and "follow" in real-time. You will be responsible for developing a robust video-based detector (in opposition to a frame-based detector) in association with an existing tracking algorithm optimized for onboard execution. Your work will bridge the gap between high-level deep learning research and efficient embedded implementation, culminating in live flight tests where your code will drive the drone’s behavior.
Responsibilities
Detector Development: Design and train state-of-the-art object detection models (e.g., YOLO variants, lightweight Transformers) based on a sequence of frames, tailored for specific mission-critical targets.
Visual Tracking: Integrate this model into an existing end-to-end tracking algorithm (e.g., Sort/DeepSort, CSRT) that maintains lock under high-dynamics and occlusion.
Edge Optimization: Profile and optimize models using TensorRT, or NPU-specific toolchains to achieve real-time inference on low-power onboard hardware (IMX, Jetson Nano, or similar).
Data Pipeline: Curate, augment, and manage high-quality datasets, utilizing both real-world flight footage and synthetic data from simulation.
System Integration: Integrate your vision pipeline into our flight stack (C++/Python) and collaborate with the GNC team to turn your detections into actionable flight commands.
Validation: Benchmark performance using quantitative metrics and participate in field testing to validate your algorithms in diverse environmental conditions.
Requirements
Education: Currently pursuing or recently completed a Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related field with a focus on Computer Vision.
Deep Learning: Strong understanding of CNN architectures, object detection frameworks, and modern loss functions, as well as the tracking world and its problematics.
Software Engineering: Proficiency in Python (PyTorch/TensorFlow) and comfortable working in C++.
Linux/Embedded: Experience working in a Linux environment; familiarity with Git is a plus.
Problem Solving: A rigorous approach to debugging and an "engineering first" mindset - valuing performance over theoretical complexity.
Language: Fluency in English; French is a plus.
Bonus
Experience with Vision model development
Experience with NVIDIA Jetson platforms and hardware-accelerated inference.
FPV pilot experience or hobbyist interest in UAVs.
Previous experience with synthetic data generation (e.g., NVIDIA Isaac Sim, Gazebo).
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.