AI/ML Engineer (Multiple)

Apex Tk•Chantilly, VA

17d

About The Position

APEX TK is seeking a motivated and skilled AI/ML Engineer to support a critical government client on aerospace-domain projects. The role works with a modeling and simulation team to conduct analyses supporting government satellite programs in Earth orbits and develops machine-learning algorithms for space-surveillance and satellite operations applications. Applications include recognizing satellite types using optical and radar space-surveillance sensors, scheduling sensor networks to find, recognize, and track maneuvering satellites, and determining activities satellites should undertake. The work includes exploratory data analysis; designing, developing, and evaluating supervised, unsupervised, and reinforcement-learning algorithms; building, training, and tuning predictive models; implementing algorithms for anomaly detection, prediction, classification, optimization, and clustering; and developing scalable, monitored high-performance-computing pipelines. The engineer collaborates with satellite designers, operations planners, and experts in astrodynamics, sensors, AI/ML, and high-performance computing. The role uses programming languages such as Python and C++ to leverage existing analysis tools and develop new ones, conducts independent trade studies and technical assessments, reviews and validates external teams’ algorithms and results, and presents actionable technical briefings.

Requirements

2–10 years of relevant experience.
Bachelor’s degree in a STEM-related field, such as engineering, mathematics, physics, aerospace, or computer science.
Active Top Secret/SCI clearance with CI Poly.
Experience performing space-related engineering analysis, including collecting, organizing, and analyzing data, using one or more of C++, Python, R, SQL, PostgreSQL, Linux, MATLAB, VBA, or STK.
Ability to communicate findings and recommendations to internal and external customers.

Nice To Haves

Master’s degree in a STEM-related field.
Experience with other simulation tools and programming languages.
Experience with phenomenological models, including the ability to model relevant physical phenomena and validate those models.

Responsibilities

Conduct analyses in support of government satellite programs in Earth orbits with the modeling and simulation team.
Develop machine-learning algorithms for satellite recognition, space-surveillance sensor scheduling and tracking, and satellite activity planning.
Conduct exploratory data analysis to identify patterns.
Design, develop, and evaluate supervised, unsupervised, and reinforcement-learning algorithms.
Build, train, and tune predictive models.
Collaborate with cross-functional teams to identify and solve problems.
Implement and optimize algorithms for anomaly detection, prediction, classification, optimization, and clustering.
Develop scalable high-performance-computing pipelines and monitoring.
Work with satellite designers, operations planners, and experts in astrodynamics, sensors, AI/ML, and high-performance computing.
Use programming languages such as Python and C++ to leverage existing analysis tools and develop new tools.
Conduct independent trade studies and technical assessments.
Review and validate algorithms and results provided by external teams.
Develop and present actionable technical briefings.