Most perception systems still rely on expensive hardware, specialist supply chains, and technology that has changed surprisingly little in decades.
This is a deep tech startup in stealth mode, backed by leading European investors, building novel perception systems for defence and security – pushing more capability into software and deploying it at the edge.
The team is currently seven people and growing. They bring experience across defence, deployed AI systems, security, and hardware acceleration, including real-world deployments at organisations such as Palantir and Helsing.
The work is classical and modern computer vision. They’re building models that run on severely constrained edge hardware, where power, latency, and memory are hard limits. Custom silicon acceleration is part of the stack.
What you’ll do
- Design and develop models for image enhancement, processing, and understanding in degraded visual environments
- Optimise architectures for constrained edge hardware, balancing accuracy against latency, power, and memory
- Apply quantisation, pruning, and compression techniques to hit strict power and latency targets
- Build robust training pipelines and curate datasets that capture real-world edge cases and failure modes
- Work across a model family spanning compact architectures up to transformer and SSM approaches
- Collaborate with FPGA and hardware engineers to validate model–accelerator integration
- Translate field performance and failure modes into better models and tighter training signals
- Help shape the research direction as the lab scales
What you’ll need
- Strong background in computer vision – classical and modern methods, with demonstrated research impact in areas such as object detection, segmentation, image enhancement, or SLAM
- Hands‑on experience optimising models for constrained hardware: quantisation, pruning, distillation
- Solid Python and deep learning framework experience (PyTorch or similar), with a track record of taking models from research code to optimised inference
- Track record of building and shipping AI systems, not just publishing
- Genuine interest in defence and security as a mission area
- Applied mindset – you work at the edge of what’s possible and deliver results
Bonus
- PhD in computer vision, machine learning, or a related field
- Experience with transformer or SSM (Mamba) architectures
- Familiarity with hardware‑accelerated inference, FPGA/ASIC deployment, or custom silicon pipelines
- Background in signal processing, SLAM, or low‑level sensor integration
Shortlisted candidates will be contacted within 48 hours.
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