Senior Algorithm AI-CV Developer

We are IronBrain, a revolutionary joint-venture company on a mission to transform the Defense Industry with cutting-edge Autonomous AI technology. We are backed by the expertise of two leading companies: a leader in aerospace and defense solutions, and Cortica, a global leader in Autonomous AI.

Join our Core Team

As a senior AI & CV Algorithm Developer, you'll play a pivotal role in shaping the future of IronBrain. You'll be part of a select team responsible for research, develop and deploy state-of-the-art AI solutions for the defence segment.

What We Do

Our current focus is on aerial computer vision, leveraging Synthetic Aperture Radar (SAR) from satellites and UAVs, as well as camera data from drones and UAVs. We're building the foundation for the future, with plans to expand into radars, UGVs, robotics, and more.

What You'll Do

• Work on complex real life problems in the field of CV using state-of-the-art AI methods.
• Research/Develop/Deploy cutting-edge computer vision & NLP algorithms using AI models for different tasks, e.g., object detection, classification, and representation learning.
• Work in a large scale computing environment (on-prem and cloud).
• Be an integral part of our founding core team, shaping the company's technological direction.

Requirements

• 4+ years of experience as an CV-AI Algorithm developer in the industry
• 3+ years of experience in relevant AI algorithm domains (VLMs, few-shot learning, continuous learning, representation learning, video analysis, object detection/tracking, etc.)
• Deep understanding of classical computer vision techniques and their applications.
• Proven ability to design, implement, and evaluate complex AI systems.
• Proficiency with Python and PyTorch
• Full-time work from our Tel Aviv office is required
• Willingness to undergo a security clearance

Advantages

• Experience in aerial vision (drones, UAVs, satellites, EO, SAR, IR)
• Experience in representation learning (embeddings/feature vectors)
• Experience with few-shot learning and self-supervised learning