Presien is an AI vision company solving real heavy industry challenges, from quality assurance and control to security and safety. Presien improves how heavy industry goes to work. 

Depending on your skills, experience and interests, you could be working on any of the following:

• Maintain and improve our datasets, including through the continuous development of our active learning pipeline 
• Refine and debug our MLOps pipelines and repositories 
• Work on detection model and pipeline issues reported in the field and implement solutions 
• Maintain and develop code for dataset generation and maintenance, model training, and evaluation and testing
• Maintain and develop code for our detection pipeline 
• Develop novel AI/ML and computer vision solutions for new features and products

Please note that applicants MUST have an existing, valid visa to work in Australia. Sorry, but we are unable to sponsor overseas applicants at this stage.

Must haves

• Existing, valid visa to work in Australia. Sorry, we are unable to sponsor overseas applicants at this stage
• Extensive hands on machine learning and deep learning experience
• Good knowledge of deep learning, neural networks, machine learning, and related mathematical fields
• Experience with one or more common machine learning frameworks used for deep learning (e.g PyTorch, Tensorflow, etc…)
• Computer vision and image processing experience with application in ML (e.g. working with OpenCV, training image classification/object detection models, etc…)
• Very comfortable working on Linux systems, especially on the command line
• Experience with shell scripting
• Solid Python knowledge, including the standard library, numpy and other ML related packages
• Very comfortable with Git
• Minimum bachelor level degree in a relevant field
• Demonstrable experience of the above skills and knowledge (2 years minimum); e.g. working in industry, academia, master’s degree by research, or PhD.
• Self-motivated with an autonomous work ethic

Preferred

• Experience with working in a ML production environment (MLOps)
• R&D and/or research experience
• Experience using docker
• Experience using GPU accelerated cloud instances for training
• Experience preparing machine learning pipelines for lower power embedded systems (e.g. Nvidia Jetson)
• Some software development experience

Nice to have

• Experience with the following specific tools: Pytorch, TensorRT, DVC
• C/C++ experience
• AWS experience, specifically EC2