Randstad is recruiting a Swarm AI Engineer to join its client directly.
...swarm ai engineer.
- posted 18 november 2025
- closes 8 december 2025
key responsibilities
- Algorithm Design: You'll pioneer the creation of AI algorithms for swarm intelligence, enabling drones to communicate, collaborate, and execute tasks autonomously and in a synchronized fashion.
- Realistic Simulation & Validation: Develop and leverage simulation environments to rigorously test and confirm swarm behaviors, ensuring they're robust and efficient before live deployment.
- Dynamic Real-Time Adaptation: Implement algorithms for real-time data processing and decision-making, allowing the drone swarm to adapt fluidly to changing environments and mission demands.
- Seamless System Integration: Integrate advanced AI algorithms directly into drone hardware and software, guaranteeing flawless operation and communication across the entire swarm.
- Performance Optimization: Continually refine swarm algorithms to boost performance, scalability, and energy efficiency, always mindful of onboard processing limits and battery life.
- Collaborative Innovation: Work hand-in-hand with cross-functional teams—including hardware engineers, software developers, and field operators—to define needs and deliver solutions that hit project targets.
- Leading Edge Research: Stay on top of the latest breakthroughs in AI, swarm robotics, and multi-agent systems, applying new techniques to enhance our drone swarm capabilities.
- Thorough Documentation: Keep detailed records of algorithms, simulation outcomes, and system configurations for easy reference and future replication.
- Real-World Field Testing: Partner with our Ops teams to conduct field tests and demonstrations, evaluating drone swarm performance in real conditions and making necessary adjustments for improved functionality.
skills
- Strong command of programming languages, including Python, C++, Java, or Rust.
- Hands-on experience with popular machine learning frameworks and libraries such as TensorFlow (including Lite/TensorRT), PyTorch (including Mobile), or scikit-learn.
- Deep understanding of swarm intelligence algorithms and multi-agent systems principles.Familiarity with established robotics software frameworks like ROS (Robot Operating System).
- Knowledge of real-time operating systems (RTOS) and embedded systems.Practical experience with simulation tools such as Gazebo, Webots, or creating custom simulation environments.
- A solid grasp of communication protocols and networking for reliable drone-to-drone and drone-to-ground
- communication.Familiarity with edge computing platforms and hardware accelerators (e.g., NVIDIA Jetson, Google Coral,
- ARM Cortex), with a specific emphasis on NVIDIA Jetson Orin.
- Demonstrated experience in deploying and optimizing machine learning models for edge computing environments.
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