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Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. The company recently closed a $200M Series B at a $1.4B valuation and is expanding teams to deliver mission-critical systems to allied forces.
As a Radar Signal Processing Engineer on the Radar team in Paris, you will own the signal processing algorithms behind the company's next-generation airborne radar payload. You will take full ownership of your workstream from algorithmic research through embedded implementation and flight validation, working with high autonomy and minimal oversight.
Key responsibilities include:
- Design and implement near-real-time algorithms for radar image formation, including inverse SAR (ISAR) processing of moving targets
- Develop and refine detection and tracking algorithms (e.g., EKF/UKF-based approaches) for moving target indication on airborne radar data
- Build jamming estimation capabilities, combining out-of-band spectral sniffing with in-band spatial covariance analysis, and the adaptive logic that drives radar behavior in response
- Contribute radar-derived observables and algorithms that support navigation when GNSS is unavailable
- Prototype and port algorithms onto the selected embedded computing platform, balancing algorithmic performance against real-time and resource constraints
- Validate algorithms end-to-end through hardware-in-the-loop bench testing, anechoic chamber campaigns, and real flight conditions, iterating quickly on results
- Work closely with the EW and RF analytics modules to align signal processing development with system-level requirements
You will work in a demanding environment where rigor, ownership, and execution are expected. The role emphasizes taking on hard technical challenges and delivering real-world impact.
REQUIREMENTS:
- 2 to 5 years of confirmed, hands-on experience in radar signal processing, gained in the defense or automotive radar industry preferably
- Solid experience in signal processing on radar systems, either FMCW or pulsed; exposure to moving target tracking, ECCM, radar-based navigation, or live/real-time processing is a strong plus
- Strong theoretical foundations and genuine hands-on comfort with the underlying mathematics, not reliance on off-the-shelf tools or LLM-generated solutions
- Quick thinker, comfortable taking full ownership of a technical scope end-to-end with minimal oversight
- Comfortable working in a fast, iterative cycle: writing code, running hardware-in-the-loop tests, and pushing validated changes independently
