ANDRO Computational Solutions, LLC
Radio Frequency Engineer
Skills
25 days ago
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Important information
Contract type:
Permanent
Salary:
$90,000 – $130,000 / year
Location:
Rome, NY, USA
Work mode:
Onsite
Published on:
30 September 2026
What they need
About the Role
ANDRO Computational Solutions, LLC is seeking entry- to mid-level candidates to join our Marconi-Rosenblatt AI Innovation Lab. This position offers hands-on experience developing and testing machine learning solutions for radio frequency (RF) and wireless communication applications.
You will combine programming, RF signal processing, and machine learning to solve wireless engineering challenges, implement solutions on RF hardware, and optimize performance on embedded systems.
What You’ll Do
- Develop and test software in C/C++ and Python for RF signal processing and machine learning applications.
- Apply wireless communication and signal processing fundamentals to analyze RF signals and solve engineering problems.
- Support the development, evaluation, and implementation of machine learning models for RF applications.
- Integrate and test algorithms on software-defined radios and embedded hardware.
- Evaluate system performance, troubleshoot issues, and improve computational efficiency.
- Collaborate with scientists and engineers to document results and advance research and development projects.
Required Qualifications
- Proficiency in C/C++ and Python.
- Foundational knowledge of RF engineering and wireless communication principles.
- Understanding of basic RF signal processing concepts.
- Familiarity with machine learning fundamentals.
- Strong problem-solving skills and an interest in hands-on software and hardware development.
Preferred Qualifications
- Experience applying machine learning to RF signals or wireless communication systems.
- Hands-on experience with software-defined radios (SDRs).
- Experience implementing or optimizing algorithms on embedded systems.
- Familiarity with 5G and Open Radio Access Network (O-RAN) technologies.
- Knowledge of neural network architectures, such as convolutional, recurrent, or graph neural networks (CNNs, RNNs, or GNNs).
- Experience evaluating machine learning model performance and computational requirements on hardware.
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