Leidos

Systems Integration and Test Engineer

United States · Work style not stated · Full time

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About the role

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  • EmployerLeidos
  • LocationUnited States
  • Work styleWork style not stated
  • Employment typeFull time
  • Employer posted

The Transportation Solution Division within the Leidos Homeland group is seeking Systems Integration and Test Engineer who will be a member of a dynamic team working on one of several Air Traffic Management programs including

En-Route Automation Modernization (ERAM), the heart of the Next Generation Air Transportation System and the pulse of the National Airspace System, providing the foundation for NexGen functions

Time-Based Flow Management (TBFM), is a system used in air traffic control to manage the flow of aircraft into congested airspace by assigning a specific time for each aircraft to reach a certain point. It shifts the focus from managing separation distance to managing arrival times, allowing controllers to optimize the flow of traffic, reduce delays, and decrease fuel consumption.

Primary Responsibilities:

We're seeking a Systems Integration and Test Engineer to join our team. This is an excellent opportunity for a recent college graduate to apply their skills in a real-world setting, as you'll be responsible for testing key components of our air traffic control system. This role involves performing regression tests and developing Red Hat scripts to automate and streamline processes. The ideal candidate will have recently earned a degree in Computer Science, Software Engineering, or a related field and possess a strong understanding of software development life cycles and testing methodologies. Proficiency in Red Hat/Linux environments and scripting is essential, as are strong problem-solving skills and attention to detail. Knowledge of cloud computing, Artificial Intelligence (AI), Machine Learning (ML), or digital engineering would be a significant plus. Tasks will include assistance with system architecture, system concept development, requirements engineering, operational and decision analysis, systems definition, system risk management, and requirements baseline management. Other tasks will include the development of test approaches and steps as well as execution of tests in lab environment including integration and debugging of multiple interfaces. Tasks will also include the development of scripts and test automation tools.

Show full description

Basic Qualifications:

- Candidates must have completed a degree program in a relevant discipline; for example, engineering, computer science

- Candidates must have the ability to obtain a public trust clearance

- Candidates must be US Citizens

Preferred Qualifications:

- Familiarity with AI/ML concepts, including experience through academic projects

- Candidates must have completed a degree program in engineering or computer science

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.

Original Posting:

October 8, 2026

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

Pay Range $57,850.00 - $104,575.00

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.

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At a glance

  • Employment typeFull time
  • Employer posted
  • Added to ROLIVA
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Skills in this posting

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Machine learning

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