Vi Dimensions is a funded company and was founded in 2015 with the simple idea that video analytics can be done in a much better and efficient way, backed by data analyses and machine intelligence. Now imagine such smart surveillance on a larger scale, tapping on the vast network of cameras throughout a city and managing huge amounts of video with a minimal number of operators. From organizations to cities, Vi Dimensions is paving the way for a smarter and safer world and we are just getting started.
We are a funded start-up based in Singapore, working on computer vision and machine learning applied to videos. We have developed our own computer vision/ machine learning algorithm and systems platform to detect anomalies in videos and we have already analysed at least a few hundred thousand hours of surveillance video in real time and are quickly expanding worldwide.
The company consists of people with varying backgrounds and nationalities worldwide including PhDs with deep technical expertise. Technical competence and strong practical implementation skills related to software development is highly regarded, along with the ability to work in a team.
The engineer will be responsible for working on the intersection of computer vision and machine learning, in order to bring our products to the next level with new features and capabilities. It involves adapting and trying out the state of the art. We expect a baseline level of implementation capability as we believe that it is fundamental to iterating quickly on research ideas and also have a realistic appreciation of the state of the art methods in real life deployment scenarios.
- Any Bachelors with coursework in programming and mathematical subjects. Masters/PhD are preferred.
- A solid programming and implementation baseline in cplusplus is essential.
- Additional experience in Python / .NET is desirable.
- Good fundamentals in algorithms and data structures.
- Some mathematical background (e.g. linear algebra, probability, optimization).
- Good problem solving skills, with a rigorous approach to engineering.
- Intuition and experience in deep learning.
- Full-Time position(s) available.