ML-Engineering Lead

At D-Fend Solutions, our people are our greatest strength — and technology is our passion. Our team of innovators and subject matter experts, including veterans of elite military intelligence technology units, is transforming the way organizations protect themselves from drone threats.

As the global leader in cyber-takeover counter-drone technology, we design and deliver advanced, software-defined solutions that empower our customers to detect, take control, and safely land unauthorized drones — with precision and reliability.

Every day, we push the boundaries of innovation to meet real-world security challenges across industries and environments. We’re proud of our cutting-edge technology, our global reach, and the impact we make in securing the skies.

We’re seeking passionate professionals who thrive in a fast-paced, creative, and collaborative environment — those who want to be part of the next generation of airspace security innovation.

Join D-Fend Solutions and help make the world a safer place.

Become a D-Fender! Work with the world’s leading cyber-takeover counter-drone technology provider and shape the future of safe airspace.

What You Will Do:

• End-to-End Ownership: Lead the full technical execution of ML projects, ensuring every stage—from data acquisition to deployment—meets our rigorous standards
• R&D Collaboration: Partner closely with R&D departments to design data collection strategies and integrate ML-based tools and agents into their specialized workflows
• Versatile Implementation: Develop models and intelligent systems for a variety of use cases, ranging from real-time field performance to autonomous research-support tools
• Technical Authority: Act as a founding voice for ML implementation and strategy within the CTO’s office
  
  

• M.Sc. in Electrical Engineering, Computer Science, or a related technical field
• At least 7 years of experience building, training, and deploying ML models
• Proficiency in Python and deep familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience building AI agents (e.g., RAG systems, agentic workflows, or tool-calling implementations) is a significant plus
• Proven ability to take a machine learning task from the research phase to a fully implemented and evaluated solution