Allen Control Systems
Chief CV/ML Engineer
About this role
Lead the computer vision and machine learning architecture for an autonomous anti-drone weapon system at a defense startup founded by experienced Navy engineers. Own the technical strategy for real-time drone detection and tracking, mentor senior engineers, and drive the most challenging technical problems from concept through fielded systems.
What you'll do
- Own CV/ML technical roadmap and system architecture, setting direction for detection, tracking, and classification
- Make and document critical technical decisions on sensor selection, compute architecture, model strategy, and real-time performance tradeoffs
- Lead a small group of senior CV/ML engineers and provide technical mentorship across the organization
- Drive the hardest technical problems personally from concept through field validation
- Partner with VP of AI and Engineering Managers to align strategy with product goals and delivery timelines
- Set and enforce technical quality standards through architecture and design reviews
What they're looking for
- Computer vision and machine learning (15+ years)
- Real-time and safety-critical perception systems
- Python and C++
- PyTorch or TensorFlow
- Object detection and tracking algorithms
- Edge computing and embedded GPU platforms (NVIDIA Jetson)
- Multi-sensor fusion and signal processing
- Systems architecture and technical leadership
Benefits
- Competitive salary
- Equity package
- Health, dental, and vision insurance
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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
- Describe a complex perception or autonomy system you owned end-to-end from architecture through production—what were the key technical decisions and tradeoffs?
- How have you approached real-time performance constraints when deploying ML models on edge hardware, and what techniques have you used to optimize inference speed and accuracy?