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Added 16 mins ago
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AquaEye / VodaSafe Inc

Lead Applied Scientist – Signal Processing & Machine Learning

Vancouver, BC, Canada
Greater Vancouver, BC, Canada

$120,000 to $180,000 CAD / year · Full-time

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Job details

Pay: $120,000 to $180,000 CAD / year
Job type: Full-time
Apply by: Nov 4, 2026
Experience: Approximately 5+ years of relevant industry, applied research or product-development experience.
Posting language: English
Posted: Oct 5, 2026

Original job description

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

Design and develop detection methodologies using the most appropriate combination of signal processing, statistical methods, machine learning and algorithmic approaches.
Develop, train, validate and deploy machine learning models where ML provides meaningful value.
Evaluate both simple and complex approaches rather than assuming a particular ML architecture is the answer.
Convert scientific and algorithmic concepts into production-quality code for embedded and/or cloud environments.
Define meaningful performance metrics, with particular attention to false positives, false negatives, generalization and performance across different operating environments.
Continually experiment with and evaluate new approaches to improve detection performance.
Optimize algorithms and models for performance within embedded hardware constraints.

Data Strategy & Field Experimentation

Help define what data AquaEye needs to collect to improve its detection capabilities.
Design experiments and field-data collection initiatives that test specific hypotheses and address gaps within existing datasets.
Establish methodologies for data cleaning, preprocessing, labelling, quality assurance and validation.
Identify biases, gaps and underrepresented operating conditions within existing datasets.
Build datasets that represent the range of real-world environments in which AquaEye is deployed.
Develop rigorous approaches for evaluating whether improvements observed during development generalize to real-world field conditions.

Product Development & Technical Direction

Work alongside Product, Hardware, Embedded Software and executive leadership to define the technical roadmap for AquaEye's detection capabilities.
Translate scientific findings into practical product improvements.
Help determine when challenges should be addressed through better data, signal processing, algorithm development, machine learning, hardware changes, or a combination of approaches.
Participate in field testing, customer trials and product validation.
Communicate complex scientific and technical concepts clearly to both technical and non-technical stakeholders.
Contribute to the longer-term development of AquaEye's data science and machine learning capabilities.

Technical Leadership & Team Development

Serve as a senior technical resource within the product-development team.
Provide technical guidance and mentorship as AquaEye's data science and ML capabilities grow.
Establish rigorous scientific and engineering practices around experimentation, validation, documentation and reproducibility.
Conduct technical reviews and encourage knowledge sharing across engineering disciplines.
Help define the skills and capabilities required as the future data science/ML team expands.

Required Qualifications:

Bachelor's, Master's or PhD in Engineering, Computer Science, Mathematics, Physics, Acoustics, Signal Processing, Data Science, or another relevant quantitative discipline.
Approximately 5+ years of relevant industry, applied research or product-development experience.
Demonstrated experience working directly with raw signals, sensor data or other complex physical-world datasets.
Strong understanding of signal processing and/or time-series data analysis.
Experience investigating noisy real-world data and identifying meaningful features, patterns and sources of variability.
Strong Python skills and experience with scientific/data-processing and machine-learning libraries.
Experience developing algorithms and/or machine-learning models from initial data exploration through validation and implementation.
Experience with data cleaning, preprocessing, feature engineering, model development and experimental design.
Ability to translate mathematical or scientific concepts into working software.
Experience collaborating with hardware, software or multidisciplinary engineering teams.
Strong technical communication and documentation skills.
Curiosity and a willingness to work directly with unfamiliar data to understand why something is or isn't working, rather than treating the ML model as a black box.

Highly Valued Experience:

We don't expect candidates to have every one of these. We are particularly interested in candidates bringing depth in one or more of the following areas:

Sonar, underwater acoustics or hydroacoustics
Acoustics, audio or sound engineering
Signal processing
Radar, lidar, remote sensing or other wave-based sensing technologies
Digital signal processing (DSP)
Spectral analysis and Fourier transforms
Time-series or multidimensional sensor data
Detection, classification and pattern recognition
Embedded systems and resource-constrained computing
C or C++ development
Edge ML or embedded ML
PyTorch and/or Scikit-learn
AWS or Azure
MLOps and production model lifecycle management
Data acquisition and experimental design for physical systems
Experience with safety-critical or high-reliability products
IP development or patent strategy

Additional Assets

Previous experience taking a scientific concept from experimentation into a commercial product.
Experience conducting field experiments and collecting real-world sensor data.
Experience mentoring scientists, engineers or technical team members.
Experience helping establish or grow a data science, signal-processing or ML capability within an organization.
Comfort participating in open-water field testing year-round with appropriate PPE

Benefits

What We Offer

As a company we aim to build innovative technology that puts people and their lives first. We apply the same approach to the way we run our company. We aim to pay fairly compared to other organizations of similar size in Vancouver and we reward for growth, as we grow.

Salary range: $120,000 – $180,000
Competitive salary and performance-based incentives.
Employee ownership opportunities.
Health, dental, and vision coverage.
4 weeks paid vacation plus company closure between Dec 24 – Jan 1.
Flexible and dynamic work environment.
Opportunity to directly impact the design and development of end product
Opportunity to work on a variety of tasks and be a part of the creation process of new products

DEI Statement:

VodaSafe is a values-driven company that is deeply committed to building an equitable and diverse workforce.

We recognize that our greatest asset is our team. We encourage each team member to be their true, authentic selves. Curiosity, ambition, humility and empathy are at the base of everything we do. We welcome diverse perspectives, educational backgrounds and experiences in order to best serve our team, our customers and our community.

Inclusion Statement:

Hesitant to Apply?

VodaSafe is an equal-opportunity employer. Throughout our hiring process, we make certain that all qualified applicants will receive consideration for employment without regard to race, ethnicity, religion, skin colour, sex, sexual orientation, gender identity, national origin, age, or disability.

Research has shown that women and people of colour are less likely to apply for a position if they do not meet all of the qualifications listed in the job advertisement. At VodaSafe, we hire for potential. We recognize that no two journeys are the same - how you have gained and collected your skillset is unique to your experiences. We want to encourage you to apply even if all criteria on the job posting are not met.

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More about this job

Responsibilities

Lead analysis of raw sonar and sensor data, develop signal-processing and detection methods, and build and validate machine-learning solutions suited to real-world conditions and embedded hardware constraints. Guide data collection and field experiments, collaborate across product and engineering teams on the detection technology roadmap, and provide technical leadership and mentorship.

Requirements

Requires a relevant quantitative degree and approximately five or more years of applied industry, research, or product-development experience, including hands-on work with raw signals or complex sensor data. Candidates should have strong signal-processing or time-series analysis skills, Python proficiency, experience developing and validating algorithms or machine-learning models, and the ability to collaborate across engineering disciplines.

Skills

Signal Processing
Machine Learning
Python
Sensor Data Analysis
Time-Series Analysis
Feature Engineering
Experimental Design
Algorithm Development
Model Validation
Data Preprocessing
Acoustics
Sonar
Embedded Systems
Technical Communication
Team Mentorship
Field Testing

Benefits

Competitive Salary
Performance-Based Incentives
Employee Ownership Opportunities
Health Coverage
Dental Coverage
Vision Coverage
Four Weeks Paid Vacation
Company Closure Between December 24 And January 1
Flexible Work Environment
Opportunity To Impact Product Design And Development

Visa sponsorship

Not detected in the job text

Keywords

Sonar
Underwater Acoustics
Hydroacoustics
Signal Processing
Digital Signal Processing
Spectral Analysis
Fourier Transforms
Time-Series Analysis
Sensor Data
Feature Extraction
Machine Learning
Detection And Classification
Pattern Recognition
Python
PyTorch
Scikit-Learn
C
C++
Embedded Systems
Edge Machine Learning
AWS
Azure
MLOps
Data Acquisition
Experimental Design
Radar
Lidar
Remote Sensing
Field Testing
Model Validation

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