About the company:

Seeing Machines leverages human factors science to develop technology that measures human cognitive state in real-time. We work with the world’s leading vehicle manufacturers 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 solutions and in aviation, our advanced gaze tracking technology understands how pilots interact and monitor instruments – leading to better training and safer operations.

The Human Factors group at Seeing Machines collaborates with customers and research partners worldwide, to generate new insights that drive our technology development and validation. Our dynamic team is fast becoming an authority for regulation and policy in this space – leading the industry with best practice Human Factors research & development.

Combining Human Factors science and applied research, you will directly influence product development with our global customers. You will be part of the Seeing Machines journey as we help shape the future of operator monitoring technology and contribute to reducing driving fatalities and improving aviation safety.

About the opportunity

Location: Canberra (preferred), Melbourne

Role Type: Permanent, Ongoing

The Research Scientist (Data), Human Factors, will work directly with internal stakeholders and external customers to provide human factors and user-centred input into the design and deployment of Seeing Machines’ technology. This involves leading the design and analysis of human factors experiments that are used to improve the sensing of operator states like fatigue, intoxication, distraction, and high cognitive workload. This includes leading data collection with drivers and pilots in simulators (vehicles and airplanes), on test tracks, and in on-road or operational environments.

This role has an emphasis on the analysis of large and complex datasets to generate insights that are communicated to our algorithm and data teams.

On a typical day, you might be:

• Working with a terabyte-scale dataset to generate new classification features for operator state
• Designing an on-road experiment involving data collection with members of the public in our sophisticated test vehicles
• Presenting data on distracted driving to a major car company to help inform requirements for production driver monitoring systems
• Supervising a study on fatigued drivers using our in-house simulator
• Communicating research outcomes to algorithm and data teams who use this information to enhance our drowsiness detection algorithms
• Designing human factors experiments and statistically analysing large and complex datasets involving human operators

• Generating insights and working with our innovation teams to ensure those insights directly shape the operator state concepts and features that we develop and deliver to customers
• Clearly articulating human factors concepts and research outcomes to internal and external teams to support product deployment
• Contributing to the preparation of technical reports and peer-reviewed publications related to the analyses of Seeing Machines’ Human Factors datasets
• Presenting research outcomes and position papers to internal and external customers, industry groups and conferences
• Helping to promote Seeing Machines’ technology, credibility, and scientific leadership through the conduct and dissemination of high-quality research outcomes

About you

We value the breadth and depth of experience that our people bring to their work. We are seeking people with a good mix of technical competency and personal capability. Specifically, we are looking for people with:

• A Masters or PhD (awarded or currently completing) in human factors, psychology, ergonomics, data analytics or a related field
• Strong skills and demonstrated experience with large datasets, advanced data analysis and formal statistical techniques
• Demonstrated experience in the use of accepted methods in human factors or experimental psychology
• Experience designing, conducting, and analysing quantitative research with human participants
• Experience with Python or other scripting language
• Evidence of producing high-quality research outcomes and in disseminating effectively to academic and/or industry settings
• Evidence of ability to generate insights that have shaped positive outcomes in research, industry, and/or policy settings

Desirable

• Experience collecting and analysing data using a simulator and/or test vehicles
• Experience in conducting successful research projects (with internal and external providers)
• Understanding of the principles of research ethics
• Experience communicating complex research concepts and outcomes to internal stakeholders (e.g., Product Managers, Software & Hardware Engineers) to provide input into system design and deliver successful customer projects
• Knowledge of operator states and associated theoretical and measurement approaches
• Experience with machine learning techniques

Why Seeing Machines

• We are inventive, innovative and collaborative and are making a real difference to safety on roads, all around the world

• Work flexibly – we encourage our people to manage their work and personal lives to achieve a balanced outcome

• We are global, Seeing Machines serves a growing market in the UK, Europe, Africa, North America, Latin America, and Asia Pacific

• We are focused on employee support and understand the importance of our collective wellbeing. You will have access to our Employee Assistance Program any time you require it

• We reward and recognise achievement. You will have access to our global benefits and reward platform.

How to apply

To apply, please include an updated resume and a cover letter outlining your interest in the role and how you could make a difference at Seeing Machines.