About the role

Experienced Computer Vision Engineer building next-gen ocean farming products at Innovasea. Leading computer vision algorithms for real-time underwater analytics in hybrid capacity.

Responsibilities

Lead the design, development, evaluation, and deployment of computer vision algorithms that power real-time perception and analytics in challenging underwater environments.
Design and implement computer vision and deep learning algorithms for underwater applications (detection, tracking, segmentation, pose/key points, 3D reconstruction)
Develop robust underwater image enhancement and domain adaptation approaches (color correction, dehazing, low-light, turbidity robustness)
Create scalable training and evaluation pipelines: dataset curation, labeling workflows, augmentation, model training
Define and track algorithm KPIs (accuracy, precision/recall, tracking stability, 3D error, latency, throughput) and run ablation studies to drive continuous improvement
Optimize models for real-time deployment on edge hardware (Jetson / GPU): quantization, pruning, TensorRT , batching, pipeline profiling
Collaborate with software/hardware teams to integrate models into production systems (APIs, streaming pipelines, monitoring, versioning, rollback strategies)
Work with marine biology and ocean engineering stakeholders to ensure outputs support fish well-being, operational excellence, and scientific validity
Travel domestically/internationally for R&D site work, system validation, and customer visits

Strong foundation in computer vision + deep learning: object detection, instance/semantic segmentation, multi-object tracking
Proven experience building training/evaluation pipelines: dataset management, augmentation strategies, class imbalance handling , metrics design, test set hygiene, regression testing for models , experiment reproducibility and tracking
Proficient in Python for ML development and C++ for production integration
Hands-on experience with modern ML frameworks: PyTorch (preferred)
Experience with deployment/optimization: TensorRT / ONNX, mixed precision, quantization , performance profiling, latency/throughput optimization
Strong software engineering fundamentals: clean architecture, modular code, testing, CI/CD basics
Git-based workflows, code review culture
Excellent communication and collaboration across functions : ability to convert ambiguous problems into actionable experiments and deliverables
Ability to maintain confidentiality and work in a fast-moving environment.
Preferred / Nice-to-have
Experience with underwater vision (turbidity, low-light, motion blur, color shift), domain adaptation, or robustness methods
Experience with active learning, human-in-the-loop labeling, uncertainty estimation, drift detection
Familiarity with MLOps tooling (Weights & Biases, MLflow , DVC, data lineage, model registry)
Exposure to distributed training, multi-GPU, or cloud training workflows

Paid time off and holidays
Employee Assistance Program
Paid parental leave
Pension
Employer-paid medical, dental, vision
Wellness Allowance
Hybrid schedule option available ( must be locat ed in HRM and available to be onsite weekly )