Digital Signals Processing Engineer

Description:

Role Summary

The DSP Engineer designs, develops, tests, and integrates digital signal processing algorithms and software that support BSG's products, research efforts, and customer missions. This role works with RF, waveform, sensor, and time-series data to develop practical solutions for signal detection, processing, characterization, and classification, while collaborating closely with software, machine learning, systems, and domain experts to move concepts into reliable, deployable capabilities.

• Bachelor's degree in Electrical Engineering, Computer Engineering, Signal Processing, Applied Mathematics, Physics, or a related technical discipline; advanced degree preferred for more senior candidates.
• 5–8+ years of professional experience in digital signal processing, RF systems, communications, radar, sensing, or a related technical field.
• Strong understanding of digital signal processing fundamentals, including sampling, filtering, FFTs, spectral analysis, windowing, modulation, demodulation, and time-frequency analysis, detection and estimation theory, and stochastic processes.
• Experience working with real-world RF, waveform, sensor, or time-series data.
• Strong understanding of signal detection, characterization, feature extraction, and signal classification concepts.
• Ability to analyze noisy, incomplete, or imperfect signal environments and distinguish signal-processing limitations from downstream software or ML issues.
• Experience developing DSP algorithms and implementing them in software.
• Proficiency in Python, MATLAB, C++, or other languages commonly used for DSP development and analysis.
• Experience validating algorithms against real or representative datasets and documenting assumptions, limitations, and performance.
• Strong understanding of numerical methods, fixed-point or floating-point tradeoffs, and practical implementation constraints.
• Familiarity with software-development fundamentals including version control, testing, code review, documentation, and maintainable code.
• Ability to translate operational or customer needs into measurable signal-processing objectives.
• Strong analytical, mathematical, and problem-solving skills.
• Ability to troubleshoot complex signal-processing behavior and perform structured root-cause analysis.
• Ability to work collaboratively with software engineers, machine-learning engineers, systems engineers, and domain experts.
• Ability to provide technical guidance and informal mentorship to junior engineers on signal-processing approaches and problem-solving.
• Strong written and verbal technical communication skills.
• Ability to clearly explain signal-processing assumptions, limitations, tradeoffs, and technical risks.
• Comfortable operating in a fast-moving technical environment with evolving requirements and incomplete information.
• Demonstrated ownership, accountability, intellectual honesty, and willingness to challenge assumptions when supported by evidence.
• Ability and willingness to continuously learn new RF, DSP, software, and system concepts.

Preferred Qualifications

• Experience with RF signal detection, identification, classification, or characterization.
• Experience with radar, communications, electronic warfare, SIGINT, ISR, or other RF mission applications.
• Experience with IQ data, spectrograms, channelization, filtering, decimation, interpolation, DDC, CFAR, or similar DSP techniques.
• Experience developing signal-processing pipelines for real-time or near-real-time systems.
• Experience with SDR platforms, RF front ends, test equipment, or laboratory signal-generation and measurement tools.
• Familiarity with GNU Radio, MATLAB/Simulink, NumPy/SciPy, CUDA, or comparable DSP development environments.
• Experience implementing DSP algorithms on GPU, FPGA, embedded, edge, or other resource-constrained hardware.
• Experience with low-latency, high-throughput, or streaming data pipelines.
• Understanding of how DSP preprocessing, signal representation, and data quality affect downstream machine-learning performance.
• Experience working directly with machine-learning engineers on feature representation, labeling, training-data preparation, or model integration.
• Experience using AI-assisted engineering or coding tools to accelerate development tasks, with accountability for reviewed output.
• Familiarity with Linux-based engineering environments and containerized development.
• Experience with hardware/software integration and performance optimization.
• Experience in defense technology, government contracting, aerospace, communications, or other mission-focused technical environments.
• Active or prior U.S. security clearance, where applicable to assigned work.

Requirements:

After watching this video , if you would thrive in this type of work environment please consider applying to BSG.