Job Details

ID #44948693
State California
City Moffettfield
Job type Permanent
Salary USD TBD TBD
Source Metis Technology Solutions, Inc.
Showed 2022-08-17
Date 2022-08-16
Deadline 2022-10-15
Category Et cetera
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Software Engineer

California, Moffettfield, 94035 Moffettfield USA

Vacancy expired!

Join our team and help us reimagine air traffic management for the 21st century. This position will equally split time supporting the following two research projects:

XTM Sub-Project: The NASA xTM Sub-Project will perform Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research to enable Beyond Visual Line of Sight (BVLOS) operations and support the AAM stakeholder community. This includes research on safe and efficient UAS operations to support (e.g., urban drone delivery). The project is investigating strategic conflict resolution between reserved air corridors of different flight operators/vehicles, conformance of UAS to its designed flight volume and mitigation of ground risk due to potential failure.

The successful candidate will be supporting the following implementation of UTM ground risk mitigation path planning capability:
  • Conduct research into strategic path planning of small drones such that they avoid critical areas on the ground in the event of a failure and un-intended crash or sudden descent
  • The path planning service will provide multiple paths for a flight from a specified origin to destination with different ground risk measures and flight times, enabling a tradeoff between speedy service (e.g., shorter paths) and safer routes (e.g., longer paths that avoid critical areas on the ground)
  • Support design of an optimization method to perform the tradeoff in a scalable, efficient manner with bounds computed on performance yielded by the algorithm
  • Modern computationally efficient graph search methods such as A and RRT will be investigated and modified appropriately for the intended path planning service

Advanced Methods: FAA NextGen is working with NASA to explore using modern data analytics and machine learning technologies to enable info-centric digital National Airspace System (NAS). This includes generation of airspace constraints in standardized Exchange Model (XM) format for Standard Operating Procedures/Letters of Agreement (SOPs/LOAs) and automating the conversion of free text Notices to Airmen (NOTAMs) into digital information that can be directly fed into services for Airspace Users (AUs).

The successful candidate will also conduct research to evaluate ways to improve the NAS and TFM using innovative technologies such as machine learning, predictive analytics, and artificial intelligence:
  • Conduct concept engineering and feasibility analysis on the application of advanced technologies to Traffic Flow Management (TFM)
  • Build on the concept and prototype development already completed for Advanced Methods to propose capabilities to improve the FAA’s TFM system. The additional development should utilize leveraging FAA data sources along with candidate technologies to provide improvements to the system.
  • Develop and test prototypes and additional capabilities to test advanced technologies
  • Complete recommendations and lessons learned from development and prototyping activities

Education/Experience/Skills Requirement
  • Bachelor’s degree or higher in aerospace, electrical engineering, mechanical engineering, or computer science. Advanced degree preferred.
  • Experience in machine learning (ML) and/or artificial intelligence (AI) data science technologies
  • Experience with software development using Python, Java, JavaScript, C/C
  • Possess analytical and problem-solving skills for the design, creation, and testing of custom software
  • Experience working with aviation datasets, air traffic control, autonomous systems preferred
  • Use of natural language processing tools/framework desirable
  • Excellent organizational and communication skills
  • US Citizen or permanent resident

Other Desired Skills:
  • Familiarity with UAS technology
  • Practical knowledge of agile software development methodologies (e.g., XP, scrum)

This video provides an overview of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research capability at NASA Ames:

https://www.youtube.com/watch?v=vepo3q87Grc

EEOE including Vets and disability

Visa sponsorship is not available for this position

Candidate must work within local commuting area of Ames Research Center in Mountain View, CA.

Vacancy expired!

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