Job Description
ESC is seeking a Mid-Level Machine Learning Engineer to support a mission-focused R&D program developing advanced signal detection and classification capabilities for national security applications. This role focuses on designing, training, and deploying ML models capable of identifying complex signals within high-bandwidth sensors and I/Q data streams. The engineer will work closely with researchers and software engineers to transition prototype algorithms into low-latency, edge-deployed operational systems supporting real-world mission environments. Responsibilities: Design, develop, and optimize machine learning models for signal detection, classification, and anomaly detection within noisy and high-volume data streams Develop and tune deep learning architectures including CNNs, LSTMs, and Transformer-based models for temporal and sequence-based analysis Apply signal processing techniques such as Fourier and wavelet transforms to support feature extraction and model performance Build scalable data pipelines for real-time I/Q stream processing, including buffering, windowing, normalization, and inference workflows Evaluate model effectiveness using advanced performance metrics including ROC/AUC, precision-recall curves, confusion matrices, and other techniques for imbalanced datasets Optimize machine learning models for low-latency execution on edge and embedded hardware platforms Develop modular, maintainable, and testable code using Python, NumPy, PyTorch and/or TensorFlow Support integration with network-attached sensors, hardware abstraction layers, and real-time data sources Collaborate with software engineers, researchers, and mission stakeholders in an agile R&D environment Participate in code reviews, technical discussions, and continuous improvement efforts using GitLab-based development workflows Support containerized application development and deployment using Docker within Linux/Unix environments Required Qualifications: Experience: 4-7 years of professional experience in machine learning or data science, with at least 2 years focused on sensor-based or temporal data. Education: B.S. or M.S. in computer science, data science, or applied mathematics. Security clearance: Active Top Secret (TS) clearance required. SCI preferred. Hands-on experience developing and deploying machine learning models using PyTorch and/or TensorFlow Strong understanding of machine learning fundamentals, statistics, linear algebra, and probability Experience developing software in Linux/Unix environments Proficiency in Python and scientific computing libraries such as NumPy Experience with version control and collaborative development workflows Experience working with I/Q data streams and real-time inference pipelines ESC offers a competitive compensation package that includes premium health, dental, and vision insurance, a 401(k) plan with company match, life insurance, short- and long-term disability coverage, and more. We also prioritize work-life balance, supporting our team in maintaining a healthy blend of professional and personal well-being. PAY TRANSPARENCY NONDISCRIMINATION PROVISION Eiden Systems Consulting (ESC) is an equal opportunity employer and is committed to creating an inclusive and respectful workplace. ESC does not discriminate against any employee or applicant based on age, color, disability, gender, national origin, race, religion, sexual orientation, veteran status, or any other classification protected by federal, state, or local law. In accordance with 41 CFR 60-1.35(c), ESC will not discharge or otherwise discriminate against employees or applicants for discussing, disclosing, or inquiring about their own pay or the pay of another employee or applicant. However, employees who have access to compensation information as part of their essential job functions may not disclose the pay of others to individuals who do not have authorized access—unless such disclosure is made (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or legal action (including those conducted by ESC), or (c) as otherwise required by law.