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Head of Machine Learning
AquaEye / VodaSafe Inc
British Columbia, CAN
Full-time
Director
Onsite
Discovered 4 weeks ago
PythonPyTorchScikit-learnMachine LearningAISignal Processing
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Role Overview
AquaEye seeks a Head of Machine Learning to lead ML strategy, oversee end-to-end development, and solve embedded ML problems.
The role involves defining ML strategy, leading ML platform development, and ensuring robust ML infrastructure.
The ideal candidate will have 5-10 years of experience in ML, AI, and software development, with strong proficiency in Python and experience with cloud platforms.
Key Skills for This Role
PythonPyTorchScikit-learnMachine LearningAISignal Processing
Full Job Posting
About the Role
AquaEye seeks a Head of Machine Learning to lead ML strategy, oversee end-to-end development, and solve embedded ML problems.
The role involves defining ML strategy, leading ML platform development, and ensuring robust ML infrastructure.
The ideal candidate will have 5-10 years of experience in ML, AI, and software development, with strong proficiency in Python and experience with cloud platforms.
Responsibilities
- 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.
- 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.
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