Senior Algorithm AI-CV Developer

9 hours ago

Tel Aviv, Tel Aviv, Israel IronBrain Full time ₪120,000 - ₪180,000 per year

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 Geo-Spatial & Tactical Domains. Leveraging SAR, EO, IR & Hyperspectral Imagery from Satellites to UAVs, Drones and Tactical-Platforms. With roadmap expanding to
Multi-Modal operations,
Radars & EW.

What You'll Do

  • Work on complex real life problems in the field of CV using state-of-the-art AI methods.
  • Research, Develop, Productize & Deploy cutting-edge CV algorithms using AI models.
  • Work in a wide computing environments (On-prem, cloud & edge).
  • 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 (Perception / Self-Supervision /
    representation learning /
    Zero/Few-shot / continuous learning / video analysis /
    VLMs /
    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 geospatial / satellite / aerial vision (EO, SAR, IR)
  • Experience in representation learning (embeddings/feature vectors)
  • Experience with few-shot learning and self-supervised learning

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