The company is building autonomous machines for defense markets and needs a Lead/Principal AI Engineer to design, develop, and deploy low-latency machine learning models for real-time detection, tracking, and classification of hostile FPV suicide drones.
Requirements
- Non-negotiable proficiency in Python.
- Extensive experience with deep learning frameworks such as PyTorch and TensorFlow.
- Deep understanding of classification models, neural network architectures, and demonstrated experience with sensor fusion.
- Practical experience with the entire machine learning lifecycle, including model optimization for edge deployment and resource-constrained environments.
- Strong knowledge of linear algebra, calculus, and statistics necessary for debugging, optimizing, and writing core machine learning algorithms.
Responsibilities
- Design, train, and optimize novel neural network architectures specifically for rapid threat classification (e.g., differentiating hostile drones from environmental clutter).
- Implement robust sensor fusion techniques to combine and interpret disparate data streams (radar, RF, acoustic, and optical sensors).
- Drive MLOps and edge deployment strategies, ensuring machine learning models are efficiently deployed, monitored, and updated in the field on low-SWaP hardware.
- Apply advanced machine learning techniques, particularly anomaly detection (AD), to ensure the system can adapt to and identify new, custom-built drone threats not present in the training data.
- Collaborate with the robotics and computer vision teams to ensure model output seamlessly translates into real-time trajectory predictions for interceptor guidance.
Other
- Must be willing to submit to a background check.
- Compliance: This position requires access to export-controlled information under ITAR. Only U.S. persons are permitted to access such information.
- Security clearance or ability to obtain one
- Prior defense startup experience
- Passion for building robots or engineering projects as a hobby
