Lead Applied Scientist - Signal Processing & Machine Learning- AquaEye

Aqua Eye is a fast-growing company transforming the global water rescue industry by developing rapid-deployment, intelligent sonar solutions. Our flagship product, Aqua Eye, is a handheld sonar device with built-in AI detection to help first responders locate drowning victims faster and more effectively. As we continue to expand our global reach and develop new product lines, we are seeking a driven, hands-on Head of Machine Learning to own and advance innovation in our product’s algorithms.

This role is central to Aqua Eye’s core technology and strategic vision. The successful candidate will be responsible for leading ML strategy, overseeing end-to-end development, and solving some of our most challenging embedded ML problems.

Job Responsibilities

• ML Strategy and Technical Direction
• Machine Learning Platform & Model Development
• Data Strategy & Dataset Governance
• Product Innovation & Cross-Functional Leadership
• Team Leadership & Organizational Development

• ML Strategy and Technical Direction
• Define and execute our machine learning strategy aligned with evolving product and business objectives.
• Lead the design and evolution of signal processing and machine learning architectures for production systems.
• Establish technical standards, best practices, and development processes for ML systems.
• Evaluate emerging machine learning technologies and identify opportunities to enhance product capabilities and competitive advantage.
• Provide technical leadership on architecture decisions, model selection, and system performance optimization.

• Machine Learning Platform & Model Development
• Oversee the development, validation, deployment, and lifecycle management of machine learning models.
• Oversee the design, optimization, and scalability of our signal processing pipelines.
• Define model performance metrics and continuously drive improvements through rigorous evaluation and experimentation.
• Ensure robustness, maintainability, and scalability of production ML infrastructure, data pipelines, and supporting databases.
• Oversee ML Ops practices, including model versioning, reproducibility, monitoring, and continuous improvement.

• Data Strategy & Dataset Governance
• Establish standards for dataset acquisition, quality, governance, and lifecycle management.
• Lead planning and execution of field data collection initiatives to ensure datasets meet product development and validation objectives.
• Continually innovate on existing methodologies for data labeling, preprocessing, quality assurance, and representativeness across operational scenarios.
• Oversee continuous expansion and refinement of training datasets to improve model accuracy and generalization.

• Product Innovation & Cross-Functional Leadership
• Work alongside Product Management, Engineering, and executive leadership to define the AI roadmap and prioritize development initiatives.
• Translate customer needs and operational challenges into innovative machine learning solutions and product capabilities.
• Provide technical leadership during customer demonstrations, field trials, and critical deployments.
• Serve as the organization's subject matter expert for machine learning technologies, advising stakeholders on technical direction and product strategy.

• Team Leadership & Organizational Development
• Provide leadership and mentorship to develop a high performing machine learning team within the product development group.
• Establish project priorities, resource allocation, and development plans to ensure successful delivery of strategic objectives.
• Foster a culture of technical excellence, innovation, collaboration, and continuous learning.
• Drive project execution through effective planning, risk management, and use of project management tools such as Jira.
• Build organizational capability by defining…