RF Systems Engineer

Responsibilities

• Drive technical direction and execution of RF efforts, identifying priorities and risks in ambiguous environments
• Own end-to-end RF system performance across prototype platforms from concept through testing and validation
• Lead the development of EMI/EMC mitigation and RF hardening strategies for complex electromagnetic environments
• Build and maintain RF simulation frameworks, including antenna, propagation, and channel models
• Design and execute experiments to characterize performance, validate models, and uncover failure modes
• Collaborate with Machine Learning and hardware teams to align system design with algorithm development

Requirements

• BS or higher in Electrical Engineering, Physics, or a similar field
• Experience with RF simulation tools such as HFSS, CST, MATLAB, or GNU Radio
• Hands-on experience with electronics laboratory test equipment (spectrum analyzers, signal generators, VNAs, oscilloscopes)
• Experience designing and assembling RF signal chains, including filters, mixers, splitters, and amplifiers
• Familiarity with software-defined radio (SDR) platforms and real-world RF hardware applications
• Demonstrated ability to own technical projects or system components end-to-end

Preferred Qualifications

• Experience working with Electronic Warfare (EW) systems
• Proficiency with Python and/or Linux shell scripting
• Prior experience in a defense or government context
• Willingness to travel for field experimentation and exercise support

Benefits

• Competitive base salary and early Series A equity
• Excellent health, dental, and vision coverage
• 401(k) match up to 4%
• Flexible PTO
• Daily office lunches in NYC

About the Company

Distributed Spectrum creates systems that power the next generation of radio spectrum intelligence. We collect radio data globally, train neural networks to decipher it, and deploy them on highly optimized embedded hardware. We are a collaborative, talent-dense team solving complex problems at the intersection of signal theory, machine learning, and accelerated computing.