Senior Machine Learning Engineer

Epirus is a high-growth technology company dedicated to overcoming the asymmetric challenges inherent to the future of national security. Epirus' flagship product, Leonidas, is a software-defined system built using intelligent power management techniques which allow power-hungry systems to do more with less.  

Job Summary: Join our team to pioneer cutting-edge machine learning and deep learning solutions that will revolutionize the future of advanced electronic systems. In this role, you will tackle complex challenges in AI-driven power management, digital twin simulations, and multi-domain data analysis, driving next-generation innovation. Collaborating with world-class engineers and researchers, you will develop intelligent models that push the boundaries of real-time decision-making and electronic system intelligence. By leveraging state-of-the-art deep learning architectures and advanced optimization techniques, you will shape the future of high-performance electronic systems and intelligent automation.

• Develop and deploy models to optimize performance, automation, and intelligence in next-generation electronic systems, including intelligent power management and real-time decision-making in embedded systems.
• Develop and implement data processing techniques to enhance data quality in advanced electronic applications.
• Apply machine learning models to digital twin simulations for real-time predictive analytics and performance optimization of advanced electronic systems.
• Collaborate with System Engineers in a laboratory environment to develop and refine data collection pipelines and frameworks for training AI models.
• Work closely with cross-functional teams (e.g., software engineers, hardware developers, researchers) to integrate deep learning models into production environments.
• Develop intuitive visualization tools to facilitate AI-assisted decision-making for stakeholders.
• Deploy machine learning models on cloud and edge computing platforms to enable real-time AI processing in distributed systems.
• Stay ahead of emerging AI trends and advancements, continuously refining methodologies to enhance system intelligence and innovation.

• Bachelor’s degree in Electrical Engineering, Computer Science, Mathematics, Statistics, Physics, or a related technical field.
• Strong foundation in machine learning theory and practice, including architecture selection, feature engineering, training, validation, optimization, and deployment.
• Understanding of deep learning architectures, such as CNNs, RNNs, Transformers, and GANs, and their application in signal processing, pattern recognition, and optimization.
• Proficiency in Python programming, with experience in scientific computing libraries such as NumPy, Pandas, Matplotlib, and SciPy for data analysis and visualization.
• Hands-on experience with one or more ML frameworks, including TensorFlow, PyTorch, Keras, and/or Scikit-learn for deep learning and machine learning model development.
• Working knowledge of computer science fundamentals, including algorithms, data structures, object-oriented programming (OOP), and software development best practices.
• Experience working in collaborative, cross-functional environments, engaging with systems engineers, physicists, and software developers to integrate AI solutions into electronic systems.
• The ability to obtain and maintain a U.S. government-issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance.

• Master’s or Ph.D. in a related field with research experience in machine learning for electromagnetic modeling, power management, or digital twin simulations.
• Basic knowledge of RF systems and testing equipment, such as oscilloscopes, spectrum analyzers, vector network analyzers, and RF amplifiers to support AI-driven signal processing applications.
• Experience with optimization techniques (e.g., genetic algorithms, reinforcement learning, convex optimization, Bayesian methods) for real-time control and AI-driven system enhancements.
• Familiarity with digital twin simulation concepts, including real-time predictive modeling and AI-driven system optimizations.
• Knowledge of Linux-based systems and communication with in-house clusters.
• Experience with cloud computing platforms (AWS, Azure, or GCP) and edge computing for AI deployment in real-time embedded systems.
• Contributions to machine learning research (published papers, patents, open-source contributions) in relevant fields.

ITAR REQUIREMENTS: 

• 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.  

At Epirus, you’ll work with technical peers and great people—and get first crack at some of the defining technology challenges of our time. Here, “impossible” is just a challenge. We're a diverse, fast-growing team of change-makers fueling the future of energy with revolutionary solutions. Join us and rewrite the rules.