Voyager Technologies, Inc.
Machine Learning Engineer - Associate
About this role
Voyager Technologies seeks a Machine Learning Engineer to develop deep learning solutions for computer vision, synthetic aperture radar, and geospatial analysis supporting U.S. government customers. You'll design neural network architectures, implement object detection models, and deliver production-quality Python code with direct impact on national security applications.
What you'll do
- Research and design advanced deep neural network architectures and machine learning models
- Apply deep learning algorithms for object detection, classification, and training on domain-specific data
- Develop signal pre-processing techniques for SAR data and domain-aware preprocessing algorithms
- Create physics simulations and methods to address bias between simulated and real-world data
- Define objective functions and develop performance assessment frameworks
- Develop production-quality code primarily in Python and present findings at technical meetings
What they're looking for
- Deep learning and neural network design
- Object detection and image classification
- Python programming
- SAR (Synthetic Aperture Radar) signal processing
- Computer vision
- Machine learning model optimization and generalization
- Physics-based simulation
- Performance evaluation and metrics design
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Voyager Technologies, Inc.
Voyager Technologies builds advanced systems and technologies for defense and aerospace applications, including missile defense programs, rocket motor systems, and solid propellant technologies for space and national security. The company is hiring systems engineers, test engineers, and project engineers at multiple levels to lead technical development, oversee complex programs, and drive innovation across their defense and space portfolios.
View all jobs at Voyager Technologies, Inc.Likely interview questions
- Describe your experience designing and implementing deep neural network architectures for specific applications.
- How have you approached domain-aware preprocessing to improve model generalization in computer vision tasks?