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
Extras
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