Senior DSP / Radar Signal Processing Engineer

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; 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.
• 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.