Transportation Engineer

Description

Are you interested in improving and shaping the transportation industry with a group of intelligent and motivated individuals?  Consider joining the Leidos team operating the Federal Highway Administration (FHWA)’s Saxton Transportation Operations Laboratory (STOL), a USDOT research lab focused on the improvement of transportation operations, safety, mobility, and environmental impacts.  STOL champions the integration of emerging technologies such as cooperative driving automation (CDA) and Vehicle-to-Everything (V2X) to revolutionize transportation.

Learn about STOL here: https://highways.dot.gov/turner-fairbank-highway-research-center/labs/STOL

To support these goals, Leidos is seeking a Transportation Engineer to join a highly successful team in researching and developing Connected and Automated Vehicle (CAV) technologies to shape the future of transportation. We are looking for someone who is motivated by variety, thrives in a challenging environment, seeks professional growth, and will be a key part of a team developing and extending a variety of applications and components to improve results that impact the transportation industry.

Location:  This role will be expected to work full-time at the customer site in McLean, VA

Role Overview:
We are seeking a motivated Transportation Engineer with experience in simulation and traffic operations to join our McLean, Virginia group.  The successful candidate will engage in various projects aiming to develop and optimize the use of advanced technologies (such as intelligent transportation systems (ITS), CDA, C-V2X, CAV and AI-driven predictive modeling) to improve the safety and operation of multimodal transportation systems. 

Key Responsibilities include but are not limited to:
•    Develop, calibrate, and validate microscopic and mesoscopic traffic simulation models.
•    Apply simulation tools to assess the safety, efficiency, and environmental impacts of advanced transportation strategies, including CAV and V2X applications.
•    Support research into signal operations, signal control strategies, and traffic management applications.
•    Collaborate with internal and external development teams to identify, understand, and resolve technical goals and challenges.
•    Contribute to the development of Concept of Operations, system requirements, and technical guidance documents for transportation applications.
•    Analyze simulation outputs and large-scale datasets to explain transportation system performance.
•    Document findings through technical reports, presentations, and manuals tailored to both technical and practitioner audiences.
•    Generate synthetic data using Generative AI methods (e.g., GANs or LLMs for tabular data synthesis) using available historical data and known distributions.
•    Provide technical assistance to academia, government, and industry stakeholders in the use of ITS, CDA, and V2X technologies.
•    Support proposal development, white papers, and technical briefings.

Required Qualifications:
•    Master’s degree in Transportation Engineering, or other related fields with 2+ years’ experience.
•    At least 2 years of hands-on experience with traffic simulation software such as VISSIM and/or TransModeler.
•    Experience in signal operations and traffic control strategies, such as traffic signal timing, adaptive control, and transit priority applications.
•    Demonstrated ability to calibrate and validate simulation models.
•    At least 1 year of experience collaborating with state and local DOTs.
•    Demonstrated ability to calibrate and validate simulation models.
•    Strong analytical skills, with the ability to interpret and present simulation and traffic data.
•    At least 1 year of hands-on experience in AI/ML model development, including building, training, and deploying models using frameworks like TensorFlow, PyTorch, or scikit-learn.
•    Proficiency in at least one programming language (Python, C++, Java, VBA, or Matlab).
•    Experience writing and contributing to technical reports or research papers.
•    Strong communication skills (writing, speaking, and listening).
•    Ability to articulate solutions and rationale for design decisions.
•    Self-motivated and focused on delivering outcomes.
•    Ability to work independently and as part of large teams.
•    Ability to obtain and maintain a Public Trust clearance (which includes three years of immediate residency in the US).
•    All applicants must be legally authorized to work in the United States without company sponsorship.

Preferred Experience:
•    PhD degree in Transportation Engineering, or other related fields.
•    In-depth knowledge of CAV, Intelligent Transportation Systems, and traffic operation and management. Knowledge/experience of AI model development, including reinforcement learning, computer vision, or LLMs applied to transportation systems. 
•    Experience integrating AI/ML models with transportation simulation tools for predictive analytics, such as optimizing traffic flow or CAV decision-making. 
•    Knowledge/experience of AI model development.
•    Proficient in the application of co-simulation tools.
•    Experience developing concept of operations and system requirements documents.
•    Exposure to mesoscopic simulation tools and multimodal modeling.
•    Solid understanding of Vehicle-to-Everything (V2X) communication protocols and CDA technology architectures.
•    Demonstrated project coordination and management skills, with a track record of successfully overseeing projects from inception to completion.
•    Experience with leading or assisting in the development of technical proposals.
•    Agile methodology proficiency, with experience in applying agile practices in development environments.

Anticipated salary for this role: $80,000 - $110,000
 

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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.