Nearly 10% of all jobs in Australia are in the construction industry. Currently, there are more than 1.1 million construction staff and by May 2023 this number is set to increase another 10%. The industry, however, is not safe. Over five times more construction workers die than on site compared to Australian Defence Force casualties in Afghanistan, Iraq and East Timor. Sightdata is a Canberra-based start-up using our passion and knowledge in construction and machine learning to do.
There are four internship projects on offer from Sightdata.
Project 2: Computer Vision Engineer
This role is to extend Sightdata’s suite of deep learning based construction safety solutions. This CV internship is a key role in the expansion phase of our product. If you are successful in this application, you can expect to work to work on both classical and deep learning-based computer vision problems. Technologies that the Sightdata product is working to incorporate include, semantic/panoptic segmentation, object detection, online multi-object tracking, active learning, sythentic2real domain transfer, activity recognition, human pose estimation, facial recognition, depth estimation and people counting.
From day 1 you will own your work end-to-end from data collection, augmentation, model selection, training, hyperparameter optimisation, testing and deployment at a real-world construction site with real video data.
Within the highly specialist ML industry it is unusual to be presented an opportunity with end-to-end visibility of exactly how to go from ‘git clone’ to production AI. For those with FAANG ambitions, we believe that being able to show that you have designed, developed and deployed on an industrial issue will set you miles ahead of the competition.
To find out more about SOTA in CV please see: https://paperswithcode.com/area/computer-vision
Required technical skills
At least one-year experience programming in Python or C++.
At least one computer vision or deep learning course (at a university or online).
Experience with Unix environments, Numpy, OpenCV, PyTorch, Determined.AI, or other DL frameworks.
Basic experience with AWS is preferred.
Personal GitHub repos or contributions to existing open-source repos would be ideal.
Required/preferred professional and other skills
This project is aimed at students who are already comfortable with the basics of deep learning and want to grow and apply their skills to demonstrate a computer vision solution to an industrial problem and see immediate results. While we do not expect you to be Yann LeCunn, we certainly hope the student brings a lot of passion to learn and grow.
Remote (Intern engages on project in a remote capacity)
Only students located within Australia
Project’s Special Requirements/ Conditions
Intern must be residing in Australia during the duration of the internship placement (the requirement amended by the Host - Sightdata for Round 2)
Type of internship
How to apply
Applications are invited from eligible students to apply for the Computing Internship courses COMP3280 or COMP8830. Eligibility details of COMP3280/ COMP8830 and further information about the Computing Internship can be found on the Computing Internship page.
- Eligible students can apply through the Computing Internship application form which will be available via the Computing Internship page between 10 May and 17 May. You can nominate multiple preferred Internship projects and host organisations through the one online application form.
- Eligibility and Room Available in degree to undertake COMP3280/COMP8830 will be assessed at the time of application. If you do not meet the eligibility criteria or do not have room in your degree to fit COMP3280/COMP8830, your application will not be progressed.
- Your application will require you to upload the following documents:
- an updated copy of your Resume, and
- an Expression of Interest (limit 350 words) for each organisation you wish to apply to (for organisations with multiple projects only submit one Expression of Interest but state clearly which project/s you wish to be considered for).
Round 2 Applications open on Tuesday 10 May 2022 and close on Tuesday 17 May 2022.