AIML Engineer Multiple

APEX TK LLC; Chantilly (VA); On-site; USD 120,000 - 180,000; Full time

Job summary

APEX TK LLC seeks a Radar Signals Engineer to support a critical government satellite program. You will work with modeling and simulation teams to develop ML algorithms for recognizing, tracking, and planning satellite activities in Earth orbits.

Responsibilities include data analysis, algorithm design, HPC pipeline development, and cross‑functional collaboration with astrodynamics, sensors, AI/ML and HPC experts. A TS/SCI with CI Poly clearance is required.

Qualifications

• Experience performing space-related engineering analysis using data collection, organization and analysis.
• Ability to communicate findings and recommendations to internal and external customers.
• Experience designing, developing and evaluating ML algorithms for space domain tasks.

Responsibilities

• Collaborate with modeling and simulation teams to analyze government satellite programs in Earth orbits.
• Develop ML algorithms for recognition, tracking, and planning satellite activities.
• Conduct Exploratory Data Analysis to identify patterns and build predictive models.
• Design and optimize algorithms for anomaly detection, prediction, classification and clustering.
• Develop HPC pipelines for scalability and monitoring of analyses.
• Present actionable technical briefings to stakeholders.

Skills

Analytical thinking

Clear communication

Team collaboration

Education

Bachelor's degree in STEM

Tools

Python

C++

MATLAB

STK

SQL

Linux

PostgreSQL

Job description

APEX TK is seeking a highly motivated and skilled Radar Signals Engineer to join our team supporting a critical government client. This position offers the opportunity to work on cutting-edge projects within the aerospace domain.

Responsibilities:

• Work with our modeling and simulation team to conduct a wide variety of analyses in support of government satellite programs in Earth orbits
• Develop new Machine Learning (ML) algorithms for applications such as
  • Using optical and radar space-surveillance sensors to recognize different types of satellites
  • Scheduling space surveillance sensor networks to find, recognize and track maneuvering satellites
  • Deciding what activities satellites should undertake
• Algorithm development will include steps such as
  • Conducting Exploratory Data Analysis (EDA) to identify patterns
  • Designing, developing, and evaluating machine learning algorithms, including supervised, unsupervised, and reinforcement learning
  • Building, training, and tuning predictive models
  • Collaborating with cross-functional teams to identify and solve problems
  • Implementing and optimizing algorithms for tasks such as anomaly detection, prediction, classification, optimization, clustering
  • Developing High Performance Computing pipelines for scalability and monitoring
• Work with satellite designers and operations planners
• Work with experts in astrodynamics, sensors, AI/ML, High Performance Computing (HPC)
• Use programming languages such as Python, C++, or others to leverage existing analysis tools and develop new ones
• Conduct independent trade studies and technical assessments
• Review and validate algorithms and results provided by external teams
• Develop and present actionable technical briefings

Required Qualifications:

• 2-10 years of relevant experience.
• Bachelor's Degree in a STEM related field such as Engineering, Mathematics, Physics, Aerospace, Computer Science, etc.
• Active Top Secret/SCI w/ CI Poly Clearance
• Experience performing space-related engineering analysis (collecting, organizing, analyzing data) using one or more of the following: C++, Python, R, SQL, PostgreSQL, Linux, MATLAB, VBA, or STK
• Ability to communicate findings and recommendations to internal and external customers

Preferred Qualifications:

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