About the company:

Seeing Machines (SM) is the world leader in the field of Safety-AI and prides itself on developing technologies that save lives, for real!

Around the globe and at any time of the day, there are almost 1 million cars on the roads that are using state-of-the-art operator monitoring technology developed by Seeing Machines which provides real-time protection from distraction and drowsy-related driving events. Seeing Machines works with the world’s leading brands (eg. General Motors, Mercedes Benz, Qantas, Caterpillar, Toll) across multiple transport sectors of automotive, commercial road transport (Fleet), and aviation to enhance safety.

About The Role: 

The Senior Machine Learning Scientist will work directly with internal stakeholders to develop occupant monitoring features using state-of-the-art machine learning methods (eg NN, CNNs, RNNs), traditional computer vision techniques, or often a combination of both.

One aspect of feature development includes collaborating with Human Factors Research Scientists and Data Acquisition Specialists to help collect relevant, truthed datasets. Also required is working closely with our Performance Analysis Team to ensure that we can measure and validate the performance of the algorithms.

About The Algorithm Development Team:  

Seeing Machines works with the world’s leading OEMs to deliver state-of-the-art operator monitoring technology across our chosen transport sectors of commercial road transport (Fleet), automotive, and aviation. In Fleet, our best-in-class aftermarket product Guardian provides drivers and fleet operators real-time protection from distraction and fatigue events. In automotive, we enable safer Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) solutions. In aviation, our advanced gaze tracking technology understands how pilots interact and monitor instruments – leading to better training and safer operations.

The Algorithm Development Team brings cutting edge research in areas such as fatigue and distraction detection into occupant monitoring solutions. Take an example of fatigue detection, which is conceptually simple – just detect when someone looks tired, right? To develop this feature, there are many underlying details that need to be understood. For example, how do you collect a dataset safely? What is the true drowsiness level? How to account for individual behavioural differences? Algorithm Development Team members work closely with Human Factors Research Scientists, Data Acquisition Specialists and Machine Intelligence Researchers in order to make progress on these kinds of questions in order to develop features.

Aside from developing new features, the Algorithm Development Team also helps improve existing features that are currently used by customers. For example, making improvements to handle specific real-world corner cases, improving performance, etc.

About the role: 

• Developing proof of concept algorithms and prototypes for features that will be continually evolving in Python and C++.

• Taking the technical lead on development of one or more algorithms.

• Writing clear documentation to help define features, including use cases, dataset specifications, performance targets, etc.

• Making use of state-of-the-art machine learning techniques (eg NN, CNN, RNN) utilizing large datasets to train algorithms.

• Providing guidance to embedded software engineers to enable algorithms to be used on specific embedded hardware platforms.

• Assisting with business pursuit activities such as working with potential partners, assisting customers to evaluate our technology and fielding questions from customers.

• Responding to customer-reported issues when required in order to fix problems or limitations encountered in real-world conditions.

Educational Qualification:

• A Masters or PhD (awarded or currently completing) in Computer Vision, Machine Learning, or equivalent industry experience

Educational Experience:

Essential

• 3+ years of industry or postdoc experience.

• Strong theoretical understanding of machine learning concepts such as CNNs, RNNs, etc.

• Experience with machine learning frameworks such as TensorFlow, PyTorch or similar.

• Experience writing code in Python and/or C++, ideally in a commercial environment.

• Evidence of producing high-quality research outcomes and in disseminating effectively to academic and industry settings.

Desirable

• Understanding of traditional computer vision and/or image processing techniques.

• Experience working on research in a commercial environment.

• Experience with practical data science or statistical analysis.
• Experience explaining and discussing technical topics with varied audiences. For example, talking with customers, internal engineers, or highly technical peers.

Personal attributes:

• Ability to communicate clearly and concisely to internal and external stakeholders.

• Strong computer literacy skills.

• Well-developed problem solving and critical thinking skills with the ability to collect, organise, analyse and disseminate significant amounts of information with attention to detail and accuracy.

• Sound analysis and interpretation of data as evidenced through well written reports.

Demonstrated time management skills as evidenced by the ability to meet tight timeframes and project deadlines.