RF / Wireless Systems Engineer

Sweep360•New York, NY

3d•$240,000•Onsite

About The Position

TL;DR — We’re building humanity’s defense layer for the AI age and are looking for an exceptional RF/signals engineer to turn noisy wireless signals into reliable, actionable intelligence. If you would have joined Palantir, Nest, or Tesla at the very early start to build an infrastructure from scratch — not just clean datasets — this is that role. Why Sweep? As intelligent machines proliferate into every part of the physical world, we humans still lack a defense layer to ensure the systems and devices we rely on remain aligned with us. We're building that layer today by deploying alongside the world’s highest-stakes teams — Olympic delegations, F1 paddocks, halftime shows, global tours, studio productions, senior government officials, and executive protection units. What we learn there becomes the foundation for a civilization-defining capability. We’re a small, talent-dense team with high ownership, high velocity, and low ego. We care deeply, move fast, and are here to build something that outlasts us. Together, we’ll redefine cyber-physical security for the AI age. What makes this role special? First dedicated RF / signal intelligence hire. You’ll define how the system interprets signals — from raw input to device-level intelligence. There is no clean dataset or ground truth — you determine what the signal means. Work across BLE, Wi-Fi, timing, and behavioral patterns to infer what isn’t explicitly visible. Solve signal problems directly with hardware and deployments as they emerge. Your output is used immediately in environments where signal is incomplete and decisions still have to be made. Gain pre-Series A ownership as one of the first 10 engineers.

Requirements

3–7 years working with wireless systems, signal processing, or applied ML.
Deep understanding of BLE, Wi-Fi, RF behavior (advertising, beacons, probes, noise).
Ability to infer device type or behavior from incomplete or obfuscated signals.
Strong Python and familiarity with ML techniques (PyTorch, NumPy, etc.)
Comfortable experimenting with unconventional approaches (e.g. signal transformations, feature extraction, data fusion) and shipping what works.
Ships under pressure — not just research.
U.S. Person status required (may involve export-controlled data).

Nice To Haves

Built RF / BLE classification systems and models from zero.
Classified unknown devices despite modern device identification challenges (e.g. MAC randomization, ephemeral identifiers, obfuscated broadcasts).
Worked on adversarial / security problems.
Built inference systems without labeled data.
Worked closely with hardware teams.
Operated outside controlled environments (not just offline pipelines).
Demonstrated unconventional problem-solving in adversarial or complex environments.