Voyager Technologies, Inc.
Machine Learning Engineer - Mid-Level
About this role
Voyager Technologies seeks a mid-level Machine Learning Engineer to develop deep learning solutions for computer vision, SAR, and geospatial analysis supporting U.S. government defense and national security missions. You'll design neural networks, implement production Python code, and directly impact mission-critical applications.
What you'll do
- Research and design advanced deep neural network architectures and machine learning models
- Apply deep learning algorithms for object detection and classification tasks
- Develop domain-aware preprocessing and signal processing techniques for SAR data
- Create physics simulations and methods to bridge the gap between simulated and real-world data
- Develop objective functions and performance assessment strategies
- Write production-quality Python code and present findings at meetings and conferences
What they're looking for
- Deep learning and neural networks
- Computer vision and image processing
- Python programming
- SAR (Synthetic Aperture Radar) signal processing
- Object detection and classification
- Model training and optimization
- Physics simulation
- Data preprocessing and feature engineering
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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
- Walk us through a deep learning project where you designed a neural network architecture from scratch—what were your design decisions and how did you validate them?
- Describe your experience with computer vision tasks like object detection or classification. What frameworks did you use and what challenges did you encounter?