About the Role
Design and implement computer vision models to interpret underwater imagery and support automated monitoring systems. Improve accuracy and efficiency of fish health and growth tracking through scalable machine learning solutions.
Responsibilities
- Develop deep learning models for object detection and segmentation in underwater environments
- Optimize image processing pipelines for performance and accuracy
- Collaborate with data scientists and software engineers to integrate models into production systems
- Analyze and annotate large-scale visual datasets for training purposes
- Evaluate model performance using quantitative metrics and real-world testing
- Iterate on model architecture based on field feedback and data quality
- Work with camera systems to understand image acquisition constraints
- Support deployment of models on edge devices and cloud infrastructure
- Maintain documentation for model development and evaluation processes
- Troubleshoot issues in data labeling and model inference workflows
Nice to Have
- Master’s or PhD in a relevant technical field
- Experience with underwater or low-visibility imaging
- Background in biological or environmental sensing applications
- Familiarity with 3D reconstruction or multi-view geometry
- Knowledge of model quantization and optimization for edge devices
- Experience with camera calibration and sensor fusion
- Published work in computer vision or machine learning conferences
- Proficiency in C++ or experience with performance-critical code
- Understanding of aquaculture or marine biology concepts
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model with office and remote flexibility
Team
Collaborative team focused on computer vision and machine learning applications
About the Role
This position focuses on building robust computer vision systems that process underwater video and still images to monitor fish size, health, and behavior. The engineer will work closely with field operations to ensure models generalize across diverse environments and lighting conditions.
What We Value
Practical problem-solving, empirical validation, and iterative development are central to our approach. We prioritize candidates who can balance research-level innovation with production-grade reliability.
Impact
The models developed in this role directly inform feeding strategies, health assessments, and sustainability practices on fish farms, contributing to more efficient and ethical aquaculture operations.
