Signal Processing Engineer (RF/Acoustics)

Cutsforth is a company focused on signal processing and data science. They are seeking a Signal Processing Engineer (RF/Acoustics) to apply data science and machine learning to analyze radio frequency and acoustic signals, transforming raw sensor data into actionable insights.

Responsibilities:

• Design and develop signal processing pipelines and machine learning models that operate on RF, acoustic, and time-series sensor data, including beamforming, BSS, spectral subtraction, matched filtering, wavelet decomposition, and time-frequency analysis techniques
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw demodulated RF and acoustic data to surface patterns and anomalies
• Analyze and interpret signals from various electrical asset monitoring systems utilizing RF, acoustic, and signal processing expertise to support fault isolation and anomaly detection
• Use asset monitoring sensor data as measurement to characterize and validate signal data
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and LRU level — identifying root causes from spectral, RF, and acoustic sensor data in complex industrial systems
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate

Requirements:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Aerospace Engineering, or a closely related engineering discipline required
• 5+ years of professional experience in data science, machine learning, or applied signal processing, with demonstrated work on RF, acoustic, ultrasonic, or communications signal data
• Direct industry experience in one or more of: Aerospace, Telecommunications, Military/Defense communications, Industrial Acoustics, or RF/Electronic Systems
• Hands-on experience with time-series and signal processing techniques, including spectral analysis, filtering, and feature extraction from raw sensor or radio data
• Proficiency in Python, including scientific computing libraries (NumPy, SciPy, pandas) and ML frameworks (scikit-learn, PyTorch, or TensorFlow)
• Demonstrated use of RF measurement and analysis workflows, including use of spectrum analyzers, network analyzers, signal generators, and oscilloscopes in a professional engineering context
• Strong analytical and problem-solving skills with the capacity to work through ambiguous or data-sparse problem spaces
• Excellent written and verbal communication skills; ability to present technical findings to non-technical audiences
• Knowledge of Electromagnetic Compliance techniques
• Master's degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Aerospace Engineering, Data Science, or a related field
• Experience with radar sensing, sonar, guided-wave radar, ultrasonic sensing, or capacitive sensing systems
• Experience working with wireless protocols (4G/LTE, 5G, or military-equivalent)
• Demonstrated ability to own an ML model from prototype through production, including monitoring and retraining
• Familiarity with beamforming, spatial filtering, or array signal processing in acoustic or RF environments
• Background in military communications systems, avionics radar, or cellular infrastructure signal analysis
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tooling (MLflow, Docker, Airflow, CI/CD pipelines)
• Experience with multimodal data fusion, edge ML deployment, or physics-informed modeling approaches
• Active participation in the broader signal processing or data science community through publications, open-source projects, or conference presentations
• Amateur (Ham) Radio license or comparable hands-on RF communications background