We're looking for an experienced AI/ML Engineer with expertise in cognitive radio to join our team. In this role, you'll be responsible for developing and implementing AI/ML algorithms for cognitive radio systems, as well as designing and optimizing software and hardware architectures to improve system performance. You'll work closely with cross-functional teams to analyze data, develop models, and deploy solutions to support our mission of creating cutting-edge cognitive radio technologies. If you're passionate about leveraging AI/ML to solve complex engineering challenges, we'd love to hear from you!

  • Bachelor’s degree in artificial intelligence, math, statistics, physics, electrical engineering, computer science, or equivalent field/technical experience

  • Current knowledge of deep learning concepts (e.g., data/task parallelism, model parallelism, transformers, etc.)

  • Current knowledge of RNNs, CNNs, LSTMS, and NLP frameworks

  • Proficiency in at least one modern deep learning framework such as PyTorch or Tensorflow

  • Knowledge of stochastic modeling techniques such as dense decision trees, Hidden Markov Models, Dynamic EFSM, etc.

  • Knowledge of statiscal modeling, e.g. proper Monte Carlo setup and observation

  • Hands-on programming experience with Python and C++

  • Know how to implement deep learning models in PyTorch/Tensflow/etc. and also be able to transcode/uplift those models into a robust C++ framework.

  • Communication skills will be important as the implementation of the deep learning models will be in conjunction with other core engineering disciplines (RF, EW, Radar, etc.)

Desired Skills


  • Master's/PhD in artificial intelligence, math, statistics, physics, economics, computer science, electrical engineering or equivalent technical field

  • Active Secret Security Clearance

  • Experience with cognitive radio and/or cognitive EW models and implementations

  • Prototyping experience with rapid multiple-iteration design techniques

  • Technical whitepaper experience (published or non-published)

  • Experience with hardware accelerated systems (GPU/FPGA/ASIC)

  • Willingness to learn a little outside their technical box

AI/ML Engineer