RF Engineer

c3 is a research-led startup based in London and Kyiv. We are expanding our team and research and are seeking an RF engineer as part of this expansion. 

Requirements

• Strong background in RF sensing and wireless signal processing: channel state information (CSI), RSSI, multi-carrier/OFDM systems, amplitude/phase extraction, multipath, and the physics of RF propagation.
• Hands-on experience with real RF/wireless hardware data. You should have dealt with noisy channel estimates, phase instability, hardware/carrier frequency offsets, antenna effects, calibration drift, and the gap between clean lab captures and messy real-world signals.
• Strong understanding of RF signal-to-information inversion: extracting physical meaning from raw wireless measurements, and reasoning rigorously about what is and isn't recoverable from a given signal.
• Experience solving temporal sensing problems: detecting sustained change in a physical environment against a learned baseline, and separating genuine events from transient or cyclical confounders.
• Familiarity with the electromagnetic behaviour of materials (attenuation, permittivity, frequency-dependent effects, multi-band sensing) is highly valuable.
• Strong Python and PyTorch skills, with experience building production-quality evaluation and inference pipelines.
• Experience with classical estimation alongside ML — Kalman/particle filtering, Bayesian inference, change-point detection, or equivalent — not only deep learning.
• Experience with signal-processing toolchains (NumPy/SciPy, spectral analysis, filtering) and, ideally, embedded RF platforms (ESP32, SDR, Nordic, or similar).
• Experience with synthetic data generation / physical simulation for augmentation and controlled failure-case generation.
• WiFi sensing, radar, RF localization, IoT sensing, wireless communications, or defence signals experience is highly relevant.
• Fluent English.
• Kyiv-based preferred; hybrid or remote considered.

What you will do

• Build robust detection pipelines that extract a reliable physical signal from cross-link RF data while remaining insensitive to everyday environmental variation.
• Handle noisy channel estimates, phase drift, packet loss, hardware offsets, and differences between devices and deployments.
• Build temporal detection and classification that distinguishes genuine sustained events from transient activity, environment-wide confounders, and cases of insufficient evidence.
• Develop confounder-rejection reasoning so that environment-wide effects are not misclassified as localized events.
• Build multi-band and cross-link inversion using physical models and learned features, followed by robust localization.
• Define confidence / failure detection for detection and localization results rather than forcing a result from bad signals.
• Develop event classification and severity scoring once the underlying signal is sufficiently reliable.
• Create synthetic perturbations and physics-based generated training/evaluation data covering known failure modes.
• Own evaluation against ground truth, including detection rate, false-positive/negative rate per unit-time, localization accuracy, timing error, and uncertainty calibration.
• Evaluate alternative sensing representations and fusion approaches — including multi-band spectral methods, time-of-flight techniques, and integration with external contextual data — where they materially improve fidelity.