AI/ML Engineer (Multiple)
At a glance
Mid-level Machine Learning Engineering role at APEX TK LLC. Chantilly · full-time · no salary stated · we estimate $154,463–$174,413.
APEX TK LLC has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Study data for government satellite programs in Earth orbit with the modeling and simulation group
- Develop ML approaches for satellite identification, sensor-network scheduling, maneuver tracking and choosing satellite activities
- Examine data patterns, build and tune predictive models, and apply methods to detection, classification, clustering and optimization
- Create scalable HPC pipelines with monitoring, using programming languages to improve existing analysis tools or build new ones
- Run technical comparisons, check algorithms and results from outside teams, and brief others on your conclusions
What you bring
- You have two to ten years of relevant experience and a bachelor’s degree in a STEM discipline
- You hold an active Top Secret/SCI clearance with CI Poly
- You have analyzed space engineering data using Python, C++ or another listed tool
- You can communicate conclusions and recommendations to customers inside and outside the organization
Who this fits
This mid-level role suits someone with space engineering analysis experience who can build ML methods and communicate technical conclusions. You’ll work with satellite designers, operations planners and specialists in astrodynamics, sensors, AI/ML and HPC. The role is based in Chantilly, United States.
From the employer
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.
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