Serve as a Geophysical Navigation Engineer for the Leidos Contested Autonomy and Navigation Branch in Huntsville, AL.  Our team’s mission is to: (1) develop and demonstrate new alternative navigation technologies to meet urgent military operational needs to operate in GPS-challenged environments, and (2) rapidly transition this technology to operational platforms to outpace the adversary threats currently being faced in the field. This role will focus on alternative navigation systems relying on magnetic and gravity fields as well as other geo-physical sources. Projects involve small teams of engineers working together in spiral or agile development environments to meet aggressive customer schedules aimed at near-term field demonstrations of emerging capabilities.  Our core competencies include sensor fusion, geophysical correlation, computer vision, HW/SW integration, and machine learning.  Typical customers include Defense Advanced Research Projects Agency (DARPA), Army Combat Capabilities Development Command (CCDC) engineering centers, Office of Naval Research (ONR), and Air Force Research Laboratory (AFRL).  We are looking for a candidate who is interested in performing challenging technical work, working with small teams of engineers, and supporting new business activities. 

Primary Responsibilities

• Develop algorithms for real-time GPS-denied navigation.
• Navigate a variety of platforms, including aircraft, ships, ground vehicles, and pedestrians.
• Use multiple sensor fusion algorithms, including extended Kalman filters, unscented Kalman filters, particle filters, and factor graphs.
• Fuse simulated and real-world sensor inputs including inertial sensors, GPS, magnetometers, altimeters, Doppler Velocity Logs, ranging radios, etc.
• Develop filter error models to accurately reflect uncertainty in sensor measurements and map fidelity
• Perform covariance analyses to predict expected system performance for a variety of sensor combinations and design reference missions
• Develop calibration algorithms to enable systems to sense the desired geophysical signal while onboard platforms operating in real-world environments
• Work within a team of 3-5 engineers to test algorithms on demonstration hardware.
• Travel with the team to demonstrate systems in real-world conditions; typically lasts 1-2 weeks and occurs 3-4 times a year.
• Present techniques, solutions, and results to the team and to customers.

Basic Qualifications

• Bachelor’s degree in advanced technical field (electrical engineering, software engineering, aerospace engineering, mechanical engineering, or equivalent) and 4+ years of prior relevant experience or master’s degree with 2+ years of prior relevant experience.
• 2+ years of relevant experience with one or more sensor fusion algorithms: extended Kalman filters, unscented Kalman filters, particle filters, or factor graphs.
• Programming & analysis experience in MATLAB.
• US Citizen able to obtain & maintain a Secret security clearance after hiring.

Preferred Qualifications

• Master’s or PhD Degree in one of the majors listed above.
• Experience with one or more of the sensors listed above, especially inertial sensors.
• Experience with geophysical map-matching algorithms and correlation techniques.
• Experience with technical task leadership in small team settings.
• Experience deploying navigation algorithms to real-time applications.
• Experience with machine learning, particularly convolutional neural networks.
• Experience with Linux, C++, and/or Python.
• Experience with version control tools, especially Git or Subversion.
• Existing US security clearance (Secret or above).

NKE

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Original Posting:

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.