Signal Processing Engineer
Posted 11 Sep 2026
Advertised as “Signal Processing Engineer (RF Processing & Analytics)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3–6 yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Field of studyApplied Mathematics, Electrical Engineering, Signal Processing (or related)
Study levelMaster's or higher
About Us
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
About the Role
As Signal Processing Engineer within RF Processing & Analytics, you own the algorithms that turn raw electromagnetic intercepts into an actionable picture of the RF environment — detecting, characterizing, and identifying the emitters around our platforms in contested environments. You work across the signals captured by our Radar, Radio, and Electronic Warfare modules, building the classification and analytics layer that turns isolated detections into a real-time electronic order of battle for operators and downstream autonomy.
Responsibilities
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Signal Detection & Characterization: Build algorithms to detect, deinterleave, and extract parameters (PRI, pulse width, frequency, modulation) from intercepted RF emissions.
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Emitter Classification & Identification: Develop classification pipelines — combining feature-based methods and machine learning — to identify emitter types and match them against threat libraries.
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Electronic Order of Battle: Design the analytics that turn individual detections into a coherent, real-time picture of emitters, tracks, and threat posture.
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AI-Assisted Analytics: Apply machine learning to modulation recognition, anomaly detection, and emitter fingerprinting where it improves classification confidence in dense, contested spectrum.
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Cross-Module Data Fusion: Work with signals captured by Radar, Radio, and Electronic Warfare to build a unified processing pipeline rather than siloed, single-sensor analysis.
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Real-Time Pipeline Engineering: Take algorithms from offline prototyping (MATLAB/Python) to real-time or near-real-time implementation, alongside software and embedded/FPGA engineers.
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Field Validation: Validate detection and classification performance against simulated and real-world RF datasets, in lab and field-test conditions.