Machine Learning Engineer - Mid-Level
Public U.S. defense and space technology company providing mission-critical propulsion, electronics, mission-management, spectrum-intelligence, and lunar-space systems.
Funding history
Investors
About Voyager Technologies, Inc.
Voyager Technologies operates across national security and space infrastructure, including propulsion and energetics, spacecraft guidance and communications, orbital and lunar systems, mission operations, and AI-enabled spectrum intelligence. Its enterprise includes lunar robotic-mobility capability through the July 2026 acquisition of Astrobotic.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Voyager Technologies is seeking a Machine Learning Engineer to research, prototype, and implement deep learning models for computer vision, synthetic aperture radar, and geospatial exploitation, while delivering production-quality Python code for U.S. government customers.
Requirements
- Must be able to obtain and maintain a U.S. government clearance and be a U.S. citizen.
- 5-15 years of applicable experience.
- Bachelor's degree in engineering or a related field.
- Master's degree preferred.
- Strong machine learning background, preferably in deep neural networks.
- Familiarity with radar sensing phenomenology preferred.
- Experience with open-source frameworks is a plus.
- Ability to program in multiple languages including Python, Matlab, and C/C++ preferred.
- Must be flexible in a small business environment.
- Must be a U.S. Person and eligible for required export authorizations under ITAR and EAR.
Responsibilities
- Research, design, prototype, and implement deep neural network and machine learning architectures.
- Train neural networks for object detection and classification.
- Develop domain-aware preprocessing algorithms to improve model generalization.
- Apply signal preprocessing techniques to synthetic aperture radar data.
- Create physics simulations and methods to address bias between simulated and real-world data.
- Develop objective functions and performance assessment plans.
- Present work at meetings and conferences.
- Develop and deliver production-quality code, mainly in Python.
Benefits
- Competitive base salary.
- Discretionary annual bonus plan.
- Paid time off.
- Comprehensive health benefits.
- Retirement savings program.
- Wellness program.