# Senior DSP / Radar Signal Processing Engineer [Rnrs Solutions](https://gurify.com/jobs?q=Rnrs%20Solutions) · Warsaw, Poland · Posted 2 months ago Senior [Apply on the original posting → (opens in a new tab)](https://jobs.ashbyhq.com/rnrs.solutions/8e19b8b0-9b65-4d83-9a38-024d5a344ddf) ## Job description Our client is a European deep-tech startup developing next-generation radar systems for the detection and tracking of small, low-flying drones. The team works across radar architecture, RF hardware, signal processing, embedded systems and data-processing software. Engineers have real ownership of technical decisions and a direct impact on system performance and product capabilities. This is a hands-on R&D environment for specialists who want to solve complex engineering problems and contribute to defence technology designed for real-world deployment. ### About the role The platform is a radar / SDR / AI-DSP system (prototype). The backend core handles data intake, memory, database, API and UI. You own the signal and algorithm side — the part everything else depends on: turning a raw I/Q stream into clean, correct, detectable targets, and feeding it to the AI core with minimal latency. Your priority: build and validate a radar signal pipeline on real or synthetic I/Q data, and define the data contract handed to the AI core (frames, dtypes, timestamps, metadata). You work hand in hand with the backend engineer on buffers, timing and latency. ### What you'll do - SDR — Select, configure and run SDR platforms (USRP / UHD); build and validate GNU Radio flows; acquire, record and analyse I/Q; FPGA programming; diagnose clipping, ADC saturation, aliasing, clock drift and sample loss. - DSP — FFT and spectral analysis, FIR/IIR filtering, decimation/interpolation, DDC/DUC, STFT and time-frequency analysis, SNR estimation, detection in noise. - Radar — Matched filtering and pulse compression, Doppler processing, coherent integration with phase coherence, range-Doppler maps, CFAR (desired), phase compensation, micro-Doppler for small-target detection. - AI integration — Define the AI-core input contract, provide reference DSP functions, validate AI output against classic DSP, and set quality metrics (SNR, Pd/Pfa, Doppler resolution, phase stability). - Backend collaboration — Agree the I/Q contract over shared memory, define frame sizes, CPI length and buffering, and trace where latency comes from (acquisition, copy, DSP, inference, write). ### What we're looking for - Hands-on with SDR and I/Q streams; fluent with USRP / UHD (device config, sample rate, gain, clock, channels, RX/TX streaming). - GNU Radio — build, test, debug signal flows. - Proficiency in working with Ettus/NI USRP B210 and X410 SDRs, including hardware configuration, RF front-end tuning, and troubleshooting. - Strong DSP fundamentals: FFT, spectral analysis, FIR/IIR, decimation/interpolation, DDC/DUC. - Radar processing: matched filtering, pulse compression, Doppler, coherent integration, phase coherence, range-Doppler. - Time-frequency analysis: STFT, spectrograms, micro-Doppler. - Signal detection in noise, SNR estimation, detection-theory basics. - Python scientific stack (NumPy, SciPy, matplotlib / Plotly); able to design experiments and write clear technical reports. ### Nice to have - Active / passive radar (reflected-signal analysis, clutter, interference). - Small / low-observable target detection; micro-Doppler (rotating parts, harmonic structure). - CFAR variants (CA-CFAR, OS-CFAR); MATLAB / Octave modelling. - Work with real measurements, not only synthetic data; GPU acceleration (CuPy, Numba, PyTorch). - Integration experience with backend, embedded, RF-hardware or ML teams. ### WHAT WE OFFER Impact Own the core signal-detection system. Your work directly shapes what the product can detect, understand, and act on. Engineering Challenges Worth Solving Work on complex radar, DSP, and signal-processing problems that are typically found only in large R&D organizations or deep-tech companies. You'll tackle hard technical challenges with direct influence on outcomes. A Team You'll Learn From Collaborate closely with scientists, engineers, and operators who combine deep technical expertise with a strong product mindset. No layers of bureaucracy—just smart people solving meaningful problems together. Ownership & Autonomy We're looking for builders, not executors. You'll have the freedom to make decisions, take responsibility, and drive critical parts of the technology forward. **Live in Rnrs Solutions’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. We remove it as soon as it disappears at source. ## More jobs like this - WP [Data Engineer](https://gurify.com/job/data-engineer-at-wpp-7ad2c6680d01) WPP · Warsaw, Poland · last week - MW [Application Support Engineer](https://gurify.com/job/application-support-engineer-at-mwdn-e47765c37c84) Mwdn · Warsaw, Poland · last week - TR [Senior DevOps Engineer](https://gurify.com/job/senior-devops-engineer-at-tripledotstudios-7f1a17616f30) Tripledotstudios · Warsaw · last week - WP [Senior Data Engineer](https://gurify.com/job/senior-data-engineer-at-wpp-4f5d1b84380a) WPP · Warsaw, Poland · last week - AW [Application Security Engineer (f/m/d)](https://gurify.com/job/application-security-engineer-f-m-d-at-awin-fb24719a88c0) Awin · Berlin, Poland · last week - TR [Senior DevOps Engineer](https://gurify.com/job/senior-devops-engineer-at-tripledotstudios-4e34ab0b5fc0) Tripledotstudios · Warsaw · last week ```json {"@context":"https://schema.org/","@type":"JobPosting","title":"Senior DSP / Radar Signal Processing Engineer","description":"\u003Cp\u003EOur client is a European deep-tech startup developing next-generation radar systems for the detection and tracking of small, low-flying drones.\u003Cbr /\u003EThe team works across radar architecture, RF hardware, signal processing, embedded systems and data-processing software. Engineers have real ownership of technical decisions and a direct impact on system performance and product capabilities.\u003Cbr /\u003EThis is a hands-on R\u0026amp;D environment for specialists who want to solve complex engineering problems and contribute to defence technology designed for real-world deployment.\u003C/p\u003E\u003Ch3\u003EAbout the role\u003C/h3\u003E\u003Cp\u003EThe platform is a radar / SDR / AI-DSP system (prototype). The backend core handles data intake, memory, database, API and UI. You own the signal and algorithm side \u2014 the part everything else depends on: turning a raw I/Q stream into clean, correct, detectable targets, and feeding it to the AI core with minimal latency.\u003C/p\u003E\u003Cp\u003EYour priority: build and validate a radar signal pipeline on real or synthetic I/Q data, and define the data contract handed to the AI core (frames, dtypes, timestamps, metadata). You work hand in hand with the backend engineer on buffers, timing and latency.\u003C/p\u003E\u003Ch3\u003EWhat you\u0026#39;ll do\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003ESDR \u2014 Select, configure and run SDR platforms (USRP / UHD); build and validate GNU Radio flows; acquire, record and analyse I/Q; FPGA programming; diagnose clipping, ADC saturation, aliasing, clock drift and sample loss.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDSP \u2014 FFT and spectral analysis, FIR/IIR filtering, decimation/interpolation, DDC/DUC, STFT and time-frequency analysis, SNR estimation, detection in noise.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ERadar \u2014 Matched filtering and pulse compression, Doppler processing, coherent integration with phase coherence, range-Doppler maps, CFAR (desired), phase compensation, micro-Doppler for small-target detection.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EAI integration \u2014 Define the AI-core input contract, provide reference DSP functions, validate AI output against classic DSP, and set quality metrics (SNR, Pd/Pfa, Doppler resolution, phase stability).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBackend collaboration \u2014 Agree the I/Q contract over shared memory, define frame sizes, CPI length and buffering, and trace where latency comes from (acquisition, copy, DSP, inference, write).\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWhat we\u0026#39;re looking for\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EHands-on with SDR and I/Q streams; fluent with USRP / UHD (device config, sample rate, gain, clock, channels, RX/TX streaming).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EGNU Radio \u2014 build, test, debug signal flows.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EProficiency in working with Ettus/NI USRP B210 and X410 SDRs, including hardware configuration, RF front-end tuning, and troubleshooting.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong DSP fundamentals: FFT, spectral analysis, FIR/IIR, decimation/interpolation, DDC/DUC.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ERadar processing: matched filtering, pulse compression, Doppler, coherent integration, phase coherence, range-Doppler.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETime-frequency analysis: STFT, spectrograms, micro-Doppler.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESignal detection in noise, SNR estimation, detection-theory basics.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPython scientific stack (NumPy, SciPy, matplotlib / Plotly); able to design experiments and write clear technical reports.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ENice to have\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EActive / passive radar (reflected-signal analysis, clutter, interference).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESmall / low-observable target detection; micro-Doppler (rotating parts, harmonic structure).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ECFAR variants (CA-CFAR, OS-CFAR); MATLAB / Octave modelling.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWork with real measurements, not only synthetic data; GPU acceleration (CuPy, Numba, PyTorch).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EIntegration experience with backend, embedded, RF-hardware or ML teams.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWHAT WE OFFER\u003C/h3\u003E\u003Cp\u003EImpact\u003Cbr /\u003EOwn the core signal-detection system. 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