
Embedded Systems & Robotics Engineer with Security Clearance
Why this role stands out
You'll drive innovation in cutting-edge autonomous tactical systems, enhancing critical hardware-software boundaries for operations in challenging environments, offering significant career growth within a reputable defense contractor. Ideal candidates are experienced embedded systems and robotics engineers with security clearance who thrive on complex problem-solving and contributing to impactful technology; apply now to join this dynamic team!
Quick overview
Job description
Embedded Systems & Robotics Engineer MAG is staffing for an Embedded Systems & Robotics Engineer who will bridge the critical hardware-software boundary for autonomous tactical systems. You'll modify and enhance systems and solutions that operate independently in GPS-denied environments, from underwater vehicles to aerial swarms, while ensuring reliability in the harshest conditions. US Citizens Only Former US Defense Contractor / US Gov / US Military Experience Only This is a Hybrid Position - Remote mainly - but as well on call to come into a MAG office when requested.
We are seeking candidates who live in proximity to our corporate HQ in Fairfax, VA primarily but will entertain persons living near our satellite offices in: Aberdeen, MD - Titusville, FL - Newport News, VA - Carthage NC Primary Responsibilities
- Enhance or augment embedded software for autonomous vehicles (UGV, UAV, UUV, USV) to extend baseline capabilities
- Implement sensor fusion for autonomous navigation (LIDAR, cameras, IMU, sonar)
- Design control systems for robotic platforms and actuators
- Create hardware abstraction layers following MOSA/SOSA standards
- Develop digital twin simulations for hardware-in-the-loop testing
- Implement real-time operating systems (RTOS) and safety-critical software
- Develop and verify safety-critical software in accordance with established standards and best practices
- Design and fabricate custom hardware solutions using 3D printing and rapid prototyping
- Apply Model-Based Systems Engineering (MBSE) practices using SysML/AADL
- Lead field testing and validation of autonomous systems
- Support deployment and troubleshooting in operational environments
- Create hardware test fixtures and validation procedures
- Document MOSA compliance and interface specifications
- Train operators on system capabilities and limitations Experience: Required Qualifications
- 5+ years embedded systems and/or robotics development experience
- Strong proficiency in C and C++ for real-time for resource-constrained systems
- Expert proficiency and practical knowledge of Python for scripting, tooling, and rapid prototyping
- Experience with ROS/ROS2 for robotics development
- Hands-on experience with autonomous vehicle platforms
- Proficiency with CAD tools and 3D printing/fabrication
- Understanding of control theory and path planning algorithms
- Experience with hardware communication protocols (CAN, I2C, SPI, UART)
- Familiarity with MOSA/SOSA standards and open architectures
- Experience with simulation tools (Gazebo, MATLAB/Simulink)
- Experience with NVIDIA Jetson platforms for autonomous systems
- Military/defense robotics experience (ROS-M, JAUS)
- Hands-on experience with RTOS (e.g., FreeRTOS, VxWorks) and/or embedded Linux (Yocto, Buildroot)
- Knowledge of digital twin technologies and real-time simulation
- Familiarity with DO-178C, DO-254, or similar safety standards
- Experience with swarm robotics and distributed control
- Understanding of SLAM and computer vision for navigation
- Experience with environmental testing (MIL-STD-810)
- Pilot's license or experience with FAA Part 107 operations Education:
- Bachelor's degree in EE, CE, ME, CS or related field Clearance:
- Must be eligible for Secret security clearance
- US Citizenship required What Makes You Successful Here
- You can go from CAD design to functioning prototype in days
- You understand autonomy from sensors to behavior planning
- You apply systems engineering rigor to rapid prototyping
- You're comfortable with both lab development and field operations
- You see MOSA as an enabler, not a constraint
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