Voyager Technologies, Inc.
Machine Learning Engineer - Associate
About this role
Voyager Technologies seeks a Machine Learning Engineer to develop advanced deep learning solutions for computer vision, synthetic aperture radar, and geospatial analysis supporting U.S. government missions. You'll design neural network architectures, implement production-quality code, and directly impact national security applications.
What you'll do
- Research and implement advanced deep neural network architectures and machine learning models
- Apply deep learning algorithms for object detection and classification tasks
- Develop domain-aware preprocessing algorithms and signal processing techniques on SAR data
- Create physics simulations and methods to bridge simulated and real-world data discrepancies
- Design objective functions and comprehensive performance assessment plans
- Develop production-quality Python code and present findings at technical meetings
What they're looking for
- Deep learning and neural network design
- Computer vision and object detection
- Python programming
- SAR (Synthetic Aperture Radar) signal processing
- Machine learning model optimization and generalization
- Physics-based simulation and domain knowledge
- Data preprocessing and feature engineering
- Performance metrics and model evaluation
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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 a deep learning project where you designed a custom neural network architecture—what problem were you solving and how did you validate performance?
- Walk us through your experience with object detection or classification models. What frameworks did you use and how did you handle generalization challenges?