Senior Machine Learning Scientist

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 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 Behavioural Features Team brings the latest AI technology to the task of understanding an operator’s behaviour (typically a driver in an automotive vehicle). The key challenge we aim to address is understanding when a driver or operator is impaired, which could be from distraction, drowsiness, intoxication or potentially other forms. Our team is responsible for identifying the relevant behaviours to measure, and how they relate to the impairment conditions, in order to detect them in real‑time. This requires both developing the impairment algorithms which run in the product and also deep analyses across large data sets to reveal valuable insights. The team is constantly striving to improve the performance of the existing algorithms, how we measure their performance, and how to collect suitable data to validate and train the algorithms. The Behavioural Features Team is multidisciplinary, composed of Machine Learning Scientists, Software Engineers, Data Scientists, Data Engineers and Human Factors Scientists who collaborate together to make progress in this area. Location : Canberra, ACT Employment Term : Full Time, Permanent The Senior Machine Learning Scientist will work directly with internal stakeholders to develop the underlying algorithms supporting behavioural features in Seeing Machines products, using state‑of‑the‑art machine learning methods (eg NN, CNN, RNN, LLM), traditional computer vision techniques, numerical processing, or often a combination of these. This algorithm development activity requires collaborating with other team members, for example: Data Engineers to collect relevant, truthed datasets Data Scientists to prepare and analyse the data, along with the relevant tooling Human Factors Scientists to understand the relevant underlying science of human behaviour Software Engineers to ensure the software is production capable If it sounds a bit like you – read on for more information below, and Apply Key Responsibilities Developing proof of concept and production algorithms 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, including model layers such as RNN, CNN, Transformers, and/or experience with model architectures such as MobileNet, ResNet, LLM, Multimodal LLM. Providing guidance to embedded software engineers to enable algorithms to be deployed on specific embedded hardware platforms. Assisting with business pursuit activities such as working with potential partners or 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. Knowledge, Skills and Experience A Masters or PhD (awarded or currently completing) in Computer Vision, Machine Learning, or equivalent industry experience Essential 3+ years of industry or postdoc experience. Strong theoretical understanding of machine learning concepts such as CNN, RNN, LLM etc. Experience with machine learning frameworks such as PyTorch, TensorFlow or similar. Experience writing code in Python and C++, ideally in a commercial environment. Evidence of producing high‑quality research outcomes and disseminating effectively in academic and industry settings. Desirable Understanding of traditional computer vision, image processing techniques, and numerical processing. 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, and highly technical peers. 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. Why Seeing Machines Being part of something meaningful – 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 Diversely Strong – 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. For more information, visit: Seeing Machines acknowledges Traditional Owners of Country throughout Australia and recognises the continuing connection to lands, waters and communities. We pay our respect to Aboriginal and Torres Strait Islander cultures; and to Elders past, present and emerging. #J-18808-Ljbffr