Allen Control Systems
Computer Vision & Machine Learning, Associate
About this role
Join a defense startup developing autonomous anti-drone systems as a Junior Computer Vision & Machine Learning engineer. You'll build real-time detection and tracking algorithms for an autonomous gun turret, collaborating with hardware engineers to integrate vision systems into a military-grade platform.
What you'll do
- Develop and optimize computer vision algorithms for drone detection, tracking, and classification in real-time
- Design machine learning models optimized for resource-constrained embedded environments
- Integrate vision systems with turret hardware in collaboration with electrical engineers
- Test and validate algorithms across various environmental conditions and scenarios
- Contribute to prototype hardening into production military-grade systems
- Assist in developing variants for different weapon systems and engagement ranges
What they're looking for
- Computer vision and machine learning
- Python and C++
- TensorFlow, PyTorch, or similar ML frameworks
- Embedded systems integration
- Real-time systems development
- Sensor integration (cameras, LIDAR, RADAR)
- Robotics experience
- Collaborative problem-solving
Benefits
- Competitive salary
- Equity package
- Health, dental, and vision insurance
- Paid time off
- Work with cutting-edge autonomous systems technology
- Engineering-first culture focused on technical excellence
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Allen Control Systems
Allen Control Systems builds autonomous defense systems, including gun turrets and drone-defense technology, using computer vision, control systems, and robotics. The company is hiring across software engineering, mechanical engineering, testing, and operations roles to support product development and scaled production.
View all jobs at Allen Control SystemsLikely interview questions
- Walk us through a computer vision project you've built from scratch—what was the problem, which algorithms or frameworks did you use, and how did you validate accuracy?
- Describe your experience optimizing ML models for resource-constrained or embedded environments. What techniques did you use to reduce latency and memory footprint?