Lead Applied Scientist - Signal Processing & Machine Learning- AquaEye

AquaEye is a fast-growing technology company transforming the global water rescue industry through rapid-deployment intelligent sonar solutions. Our flagship product, AquaEye, is a handheld sonar device designed to help first responders locate drowning victims faster and more effectively.

As we continue to advance AquaEye's detection capabilities and develop new products, we are looking for a hands-on Applied Scientist with deep experience working with complex real-world sensor data, signal processing, acoustics, sound engineering, sonar, or related scientific data.

This role sits at the intersection of signal processing, data science, machine learning and embedded product development.

We are particularly interested in someone fascinated by the data underneath the algorithm: understanding what our sonar signals contain, identifying and extracting meaningful information from noisy real-world data, determining which features matter, and using that understanding to develop effective detection methodologies and machine learning solutions.

You do not need to come from the water-rescue industry or have worked specifically with AquaEye's technology. Experience interpreting complex signals in fields such as sonar, acoustics, audio/sound engineering, radar, medical devices, telecommunications, geophysics, remote sensing, robotics, industrial sensing or other sensor-based systems may be highly transferable.

This is a highly hands-on role within a small, collaborative product-development team. You will work closely with hardware, embedded software, engineering and product leadership to help determine the next generation of AquaEye's core detection technology.

Job Responsibilities:

Signal Processing & Applied Data Science

• Lead the exploration and interpretation of raw sonar and sensor data to understand the information contained within the signal.
• Design, evaluate and improve signal-processing methodologies used to extract meaningful features from complex and noisy real-world data.
• Investigate signal characteristics across different environments and operating conditions to understand sources of noise, variability and performance degradation.
• Apply techniques such as filtering, spectral analysis, time- and frequency-domain analysis, feature extraction and Fourier transforms, where appropriate.
• Determine which characteristics of the underlying data are most useful for detection and classification.
• Develop experimental approaches to distinguish meaningful signal from environmental noise and other confounding factors.
• Work closely with hardware and embedded engineering teams to understand how sensor design, device configuration and operating conditions influence data quality.

Machine Learning & Algorithm Development