Machine Learning Scientist

at  Seeing Machines

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.

EDUCATIONAL QUALIFICATIONS:

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

PROFESSIONAL EXPERIENCE:

Essential:

• 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.

ABOUT THE ROLE:

The 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.

KEY ROLE RESPONSIBILITIES:

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