Research Computer Scientist - Artificial Intelligence Machine Learning

In support of NASA Langley Research Center’s Aeronautics Systems Engineering Branch, AMA is seeking highly skilled candidates in the field of Artificial Intelligence / Machine Learning. Specifically, AMA is seeking candidates to support work within the uncrewed aircraft systems domain developing machine learning algorithms and tools based on the use of vision and/or radar sensors for airspace surveillance, onboard sense and avoid, and perception of hazards for autonomous navigation. The selected candidate will work with NASA and regulatory authorities on the overall advancement of machine learning with the aviation industry and increase NASA’s presence in the field. This position will be located on site at NASA LaRC.

Responsibilities

• Develop algorithms to detect, track, and classify uncrewed aircraft systems (multirotor, fixed wing, or VTOL), birds, general aviation vehicles, and commercial aviation vehicles
• Integrate ground-based surveillance systems with airborne sensing suites by sharing data on an IoT platform
• Conduct literature reviews to understand state of the art for machine learning, identifying gaps for aviation
• Leverage previously collected NASA data along with future NASA and external data to evaluate machine learning techniques
• Develop sensor fusion techniques for multiple sensor types using machine learning and traditional fusing techniques
• Evaluate machine learning methodology using traditional machine learning metrics along with proposed aviation metrics by the Radio Technical Commission for Aeronautics (RTCA).
• Assist in the design and execution of experiments at NASA Langley Research Center and Armstrong Flight Research Center
• Assist in the development of machine learning challenges such as those for the Computer Vision Pattern Recognition Conference
• Document research results for conference presentation and publication

Qualifications

• MS or PhD in Computer Science or related computational field with concentrations in artificial intelligence and machine learning
• Demonstrated experience in machine learning through published research papers
• Expertise in machine learning, computer vision, sensor-based data acquisition
• Application of machine learning with the aviation domain highly desired
• Understanding of uncrewed aircraft systems operations highly desired
• Must work well in highly collaborative dynamic environments with diverse team
• Strong communications skills, written and verbal

Salary range: $100k-$150k

US citizenship or Permanent Residency is required

Analytical Mechanics Associates (AMA) is proud of our customer relationships, our diverse and dynamic work environment, and our employees' career satisfaction. AMA is a small business with a wide reach; headquartered in Hampton, VA, AMA has operations in Greenbelt, MD; Huntsville, AL; Dallas and Houston, TX; Denver, CO; Mountain View, CA; and Edwards Air Force Base, CA. With over 60 years of experience, AMA specializes in aerospace engineering, science, analytics, information technology, and visualization solutions. AMA combines the best of engineering, science, and mathematics capabilities with the latest in information technologies, visualization, and multimedia to build creative solutions. We offer competitive salaries and a substantial benefits package, including but not limited to paid personal and federally recognized holiday leave, salary deferrals into a 401(k)-matching plan with immediate vesting, tuition reimbursement, short/long term disability plans, and a variety of medical, dental, and vision insurance options.

AMA is committed to the professional growth of every employee, understanding that the successes of our employees drive our success. We provide a work environment that is engaging, collaborative, and supportive. To learn more about our company, please visit our website at and follow us on Facebook and LinkedIn.

AMA is an Equal Opportunity Employer and does not discriminate against any applicant for employment or employee because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic prohibited under federal, state, or local laws.