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Anforderungen

Strong foundation in machine learning, with experience across acoustics, sensor fusion, or computer vision Practical experience optimizing and deploying models on edge/embedded hardware Familiarity with model distillation and compression techniques Comfortable running experiments across multiple model architectures and iterating quickly For internship candidates: strong academic background in ML/AI with hands-on project experience EU work authorization Bonus Points: Experience with network or swarm intelligence algorithms Background in defense, robotics, or autonomous systems

Das wird geboten

Competitive Compensation: Highly competitive cash comp, along with virtual stock options, that reflect your experience and the value you bring. Accelerated Learning & Growth: Join a fast-paced environment where you ll take on real responsibility, overcome meaningful challenges, and grow alongside a driven team. Make a Real Impact: Play a key role in shaping Omnisent s journey and contribute directly to the success of the company.

In your cover letter, we d love to hear about your personal interests, what drives you, why this role feels like the right fit, and most importantly, why Omnisent.

Zur Position

Founded in 2025, Omnisent is a Munich-based defense technology company developing fully autonomous, Counter-UAS system. We are a small, high-output team from Cambridge, Imperial College, MIT, and BCG united by a mission to redefine modern airspace security. Tasks We are looking for an ML Engineer to develop the acoustic sensing and sensor fusion algorithms that power OmniDome s detection and classification capabilities, from early research through deployment on edge hardware.

Develop and optimize models for acoustics-based detection, sensor fusion, and swarm intelligence Work with computer vision models, testing different architectures to optimize performance Bring models onto edge hardware through optimization, quantization, and model distillation techniques Iterate on model architecture based on real-world field data and performance benchmarks Collaborate with hardware and firmware engineers to ensure models run efficiently on deployed sensors