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About the role
You will design, build, and validate the embedded core of drone detection systems using edge compute platforms. This involves developing real-time video processing pipelines, deploying deep learning models, and conducting field test campaigns.
What they look for
Requirements
The role requires 4+ years of professional experience in embedded systems development on Linux-based edge hardware. Candidates must be proficient in C/C++ and Python, with specific experience in ADAS camera systems and neural network deployment.
Benefits
Full description
About VarieneVariene develops advanced AI and computer vision software for defense and autonomous systems, designed to operate in demanding real-world environments.
Our technology brings intelligent perception to platforms operating across ISR, autonomous systems, and other mission-critical defense applications.
Our defence systems division is expanding into next-generation security, bringing intelligent perception and real-time processing to counter-unmanned aerial systems (C-UAS). We are building a dedicated counter-UAS team and are looking for both Junior and Senior Embedded Engineers to serve as our founding engineering hires. In this role, you will help design, build, and validate the embedded core of our drone detection systems from the ground up.
The RoleYou will be responsible for developing the embedded side of our detection systems using edge compute platforms.
This is a hands-on technical role that uniquely combines embedded software engineering, hardware evaluation, and real-world field validation. Rather than handing off your designs, you will own the lifecycle of the product—from bringing up sensors in the lab to testing the fully integrated system against live targets in the field.
You will work closely with our mechanical design team and machine learning engineers to translate complex detection algorithms into a ruggedized, deployable, real-time physical system.
What You'll Do• Bring up advanced ADAS camera modules and sensor interfaces on Jetson-class edge compute hardware.
- Build, maintain, and optimize the real-time video processing and detection pipeline from camera input to system output.
- Deploy, optimize, and update deep learning detection models directly on edge devices.
- Evaluate and bench-test candidate ADAS camera modules against strict performance limits (range, target speed, temperature, vibration, and long-term maintenance).
- Prepare, deploy, and execute field test campaigns at defined operational distances, environmental backgrounds, and target motions.
- Collect, record, and analyze field data across varying hardware configurations to drive iterative software changes.
- Collaborate closely with mechanical design on the physical and structural integration of the C-UAS hardware.
Knowledge and Experience• 4+ years of professional experience, with a proven track record of leading embedded systems development and hands-on hardware evaluation.
- Deep knowledge of embedded systems development on Linux-based edge hardware (NVIDIA Jetson, Xavier, or equivalent).
- High proficiency in C or C++ combined with Python.
- Hands-on experience with camera interfaces, MIPI CSI, GMSL, and real-time video streaming pipelines.
- Experience deploying and optimizing neural network models on constrained edge hardware (e.g., TensorRT, ONNX).
- A professional background in automotive systems, specifically working with companies that develop (not just assemble) ADAS camera systems.
- Excellent written and spoken English.
- Ability and willingness to travel for outdoor field testing campaigns.
Highly ValuedExperience or an interest involving:
- Live field testing of sensors, hardware, or optical systems
- Drone piloting or UAV operations (commercial or hobbyist)
- Defence, aerospace, or C-UAS industries
- Ruggedized hardware standards and environmental testing
Professional ProfileWe are looking for someone with:
- High ownership and an end-to-end engineering mindset
- Practical problem-solving skills in both lab and field environments
- Technical curiosity and a passion for cutting-edge edge-AI systems
- Structured, analytical thinking for hardware evaluation data
- Strong cross-functional collaboration and communication skills
- Persistence and resilience when troubleshooting hardware in the field
What Success Looks LikeSuccess is not measured by code commits alone. We expect you to build a reliable, field-proven detection system that performs flawlessly under real-world conditions.
You will help Variene:
- Successfully benchmark and select the optimal ADAS camera hardware;
- Achieve low-latency, real-time video pipeline performance on edge devices;
- Build a seamless model deployment workflow from training to device;
- Run autonomous, reliable field test campaigns without needing separate field staff;
- Deliver a production-ready C-UAS system ready for deployment.
- A founding engineering role with direct impact on Variene's next-generation defence product line.
- Exposure to state-of-the-art edge AI, computer vision, and counter-drone technologies.
- Direct collaboration with cross-functional engineering leadership.
- Significant technical autonomy over hardware selection, pipeline architecture, and test methodologies.
- Competitive compensation based on experience.
- Performance-related compensation.
