Research: Rapid model prototyping, training, and deployment of state-of-the-art deep learning models to solve large-scale industrial problems. You will be investigating image classification, object detection, instance segmentation, semantic segmentation, anomaly detection, and other bespoke tasks for processing industrial ultrasonic array data.
Pipeline: Work with our ML Ops team to build cloud-based pipelines for large-scale image pre-processing, data augmentation, training, post-processing, and inference.
Deploy: Work with the Software Development Team to deploy the model in production for its end-users.
Monitor: Continuously monitor the success matrices of the assigned ML project and make continuous improvements to increase model robustness and efficiency.
Data Analysis: Work closely with our Data Analysts to explore, analyze, and organize data; perform data extraction and preprocessing for training and evaluation purposes.
Document: Document model architecture, training details, dataset extraction, and cleaning procedures for reproducibility, product management, and internal training.
Requirements & Skills:
Experience developing deep learning models for computer vision tasks.
Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or an ML/AI-related field; PhD preferred.
Proficient in Python and at least one mainstream deep learning framework, such as PyTorch, TensorFlow, JAX, etc.
Experience with medical or industrial ultrasound images for deep learning-related projects.
Experience in deep learning projects for industrial non-destructive testing.
Experience leading and mentoring ML engineers and ML scientists.
Hands-on experience with ML lifecycle management tools like MLFlow, Amazon SageMaker, GPC Vertex AI, etc.
Hands-on experience with orchestration tools like Kubeflow, Prefect, Airflow, etc.
Working knowledge of Git, Docker, and cloud services like AWS and GPC.
Publications in top-tier venues like CVPR, ICCV, etc.
Great communicator with excellent data presentation, and report-writing skills.
Be able to work in a fast-paced, self-driven environment.
