Senior Machine Learning Engineer, Epirus

• Undergraduate degree in electrical engineering, computer science, mathematics, statistics, physics, or other related field
• At least 5 years of fundamental knowledge of machine learning practice and theory, including model selection, development, optimization, and deployment
• Comfortable writing robust code in Python and using Python ecosystem mathematical packages (NumPy, Pandas, Matplotlib, SciPy, etc.)
• Experience with one or more ML model development frameworks (TensorFlow, Keras, scikit-learn, PyTorch, Caffe, Apache Spark)
• Basic knowledge of computer science concepts (algorithms, data structures, object-oriented programming, etc.)
• Basic knowledge of RF systems and testing equipment such as oscilloscope, spectrum analyzer, vector network analyzer, RF amplifier
• To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here

Desirable

• In this role, you will contribute to developing advanced machine-learning software systems that support real-time hardware control, electromagnetic field simulations, and electronic beam-forming.
• Leverage your knowledge of radio frequency (RF) systems and expertise in machine learning, deep learning, and optimization to create innovative solutions for advanced electronics, electromagnetic modeling, power management, digital twin software, and data analysis across multiple domains
• Design and develop machine learning models for next-generation electronic hardware and software systems
• Build deep learning models to accelerate electromagnetic and radio frequency research
• Develop optimization models for intelligent operation and power management of robust electrical systems
• Work alongside System Engineers in a laboratory to develop a data collection pipeline and framework
• Create pattern-recognition systems for comprehending data, ensuring data quality and reliability
• Develop and apply machine learning models to digital twin software for real-time simulation of complex systems
• Collaborate with cross-functional teams to integrate deep learning models into production environments
• Work with stakeholders to develop user interfaces for controlling instruments
• Basic cloud and edge computing knowledge
• Stay current with emerging trends and advancements in machine learning and artificial intelligence technologies