AI/Computer Vision Intern - Onboard Detection & Tracking

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Entry From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Series B From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

AI/Computer Vision Intern - Onboard Detection & Tracking Paris, Île-de-France, France 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,

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