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.
