Engineering

Qualifications:

This requisition may be filled at either a Principal Software Engineer OR a Sr. Principal Software Engineer Level

Northrop Grumman Aerospace Systems has an opening for a Software Engineer - Machine Learning to join our team of qualified, diverse individuals. This position will be located in Melbourne, Florida.

Climb to new heights on your journey when you start Defining Possible with Northrop Grumman. Enjoy a purposeful career in aeronautics that is crucial to the way we connect and protect our world across land, sea, and air. Bring your experience and take advantage of this opportunity to discover how you can start to push past possible and achieve your goals today. Enjoy a diverse, collaborative environment with professionals across the nation ready to help launch your career.

Essential Functions:

• May be responsible for leading the Algorithms Product Team comprised of 2-3 engineers performing data reduction from a weapon truth data set to a representation that may be used on an aircraft to generate the Launch Acceptability Region (LAR) and Time of Flight (TOF) to the target.
• Leadership, Machine learning, Artificial Intelligence, and Python experience are crucial for this position.
• The truth data set is a large collection of points, with each point representing conditions under which the aircraft may release the weapon (altitude, winds, etc.) and whether or not the weapon can successfully engage the target under those conditions.
• Data reduction must be performed so that a computationally efficient algorithm may be used to operate on a compact data set to generate a reasonable approximation of the LAR and TOF.
• Evaluate the statistical quality of the LAR's and TOF's generated from the compact representation with the original truth data set. If the generated LAR's and/or TOF's do not meet the desired criteria, the candidate will analyze the results, identify possible root causes for the deficiencies, and improve the data reduction algorithm and/or settings to produce a better compact representation.
• Document the final LAR and TOF modeling performance statistics in the LAR Accuracy Report (LAR AR).
• Liaise with the customer to determine requirements and expectations as well as present research findings and answer questions.