Backed by QIC, Main Sequence, R+VC, Salus Ventures, Folklore Ventures, DYNE Ventures and Beaten Zone Venture Partners, Arkeus is entering a major growth phase. We are a small, fast-moving company with the energy and ambition of a start-up and the technical credibility of a world-class defence technology provider. 

We work at the intersection of optics, autonomy, robotics and real-time AI and we deploy what we build. If you want to ship technology that matters, not technology that ships decks, this is where you belong. 

Check it yourself on News | Arkeus 

We have developed an inclusive, transparent and engaging culture that focuses on building a diverse workforce — realising that to attract the very best, we need to ensure our culture is ready to welcome people from all ages, genders and backgrounds. 

About the role 

As a Machine Learning Engineer at Arkeus, you will work with the Machine Learning team to prepare data and develop ML models, joining specialists working at the intersection of machine learning, computer vision, optical science and engineering design.

Your work will be central to our image-based optical sensing technology — training, optimising and deploying models that detect and classify objects from the airborne perspective, in real time, on real hardware.

You will take ownership of models and components across the full ML lifecycle — from data pipelines through to models running on edge devices — working alongside senior ML and computer vision engineers, translating state-of-the-art research into deployed capability, and helping guide and review the work of more junior engineers as the team grows.

About you 

• An engineer with hands-on machine learning experience, ready to take on ownership and complex problems.
• A graduate in a relevant discipline, with demonstrated professional or research experience building and deploying ML models.
• Able to take a machine learning problem from idea to deployed result — independently, and demonstrated through professional work, research, or substantial personal projects.
• Enjoys reading research, experimenting and asking good questions.
• Wants to see your models running in the real world, not sitting in a notebook.

Key responsibilities 

• Implement and improve the ETL process.
• Make data readily available to engineers across the team and improve and organise our datasets.
• Use PyTorch, OpenCV and scikit-learn to design, build and train neural networks for image classification and object detection.
• Lead the implementation and optimisation of state-of-the-art deep-learned neural networks in computer vision applications.
• Contribute to and take ownership of accurate object detection across varied environments (over sea, littoral zone, rural and city) and at various target sizes.
• Participate in team meetings, conduct code reviews, and help uphold engineering standards across the team.
• Support and mentor junior engineers, sharing knowledge and good practice.
• Report software bugs and issues in the issue-tracking system in a timely manner.
• Develop and maintain documentation.

Your experience and skills 

• Tertiary qualification (Bachelor's or Master's) in computer science, engineering, mathematics or a related discipline.
• 2-4 years of demonstrated machine learning experience through professional work, research, or substantial personal projects.
• Confident working in Python, with solid hands-on experience in PyTorch.
• Strong understanding of the fundamentals of neural networks and convolutional neural networks (CNNs).
• Hands-on experience with object detection and/or segmentation problems.
• Familiarity with common libraries such as scikit-learn, scikit-image, PIL, Pandas and OpenCV.
• Sound research practices — able to read, understand and apply findings from technical literature.
• Eligibility to obtain and maintain an Australian Defence security clearance.

Desirable: 

• Exposure to C++.
• Experience with data science workflows and data pipelines.
• Technical experience reading from and controlling cameras.
• Experience deploying and optimising models on edge devices or embedded hardware.
• Exposure to airborne remote sensing, GIS or optical technologies.
• Strong physics and mathematics background.
• Examples of personal or open-source technical projects (GitHub links welcome).

Backed by QIC, Main Sequence, R+VC, Salus Ventures, Folklore Ventures, DYNE Ventures and Beaten Zone Venture Partners, Arkeus is entering a major growth phase. We are a small, fast-moving company with the energy and ambition of a start-up and the technical credibility of a world-class defence technology provider. 

We work at the intersection of optics, autonomy, robotics and real-time AI and we deploy what we build. If you want to ship technology that matters, not technology that ships decks, this is where you belong. 

Check it yourself on News | Arkeus 

We have developed an inclusive, transparent and engaging culture that focuses on building a diverse workforce — realising that to attract the very best, we need to ensure our culture is ready to welcome people from all ages, genders and backgrounds. 

About the role 

As a Machine Learning Engineer at Arkeus, you will work with the Machine Learning team to prepare data and develop ML models, joining specialists working at the intersection of machine learning, computer vision, optical science and engineering design.

Your work will be central to our image-based optical sensing technology — training, optimising and deploying models that detect and classify objects from the airborne perspective, in real time, on real hardware.

You will take ownership of models and components across the full ML lifecycle — from data pipelines through to models running on edge devices — working alongside senior ML and computer vision engineers, translating state-of-the-art research into deployed capability, and helping guide and review the work of more junior engineers as the team grows.

About you 

• An engineer with hands-on machine learning experience, ready to take on ownership and complex problems.
• A graduate in a relevant discipline, with demonstrated professional or research experience building and deploying ML models.
• Able to take a machine learning problem from idea to deployed result — independently, and demonstrated through professional work, research, or substantial personal projects.
• Enjoys reading research, experimenting and asking good questions.
• Wants to see your models running in the real world, not sitting in a notebook.

Key responsibilities 

• Implement and improve the ETL process.
• Make data readily available to engineers across the team and improve and organise our datasets.
• Use PyTorch, OpenCV and scikit-learn to design, build and train neural networks for image classification and object detection.
• Lead the implementation and optimisation of state-of-the-art deep-learned neural networks in computer vision applications.
• Contribute to and take ownership of accurate object detection across varied environments (over sea, littoral zone, rural and city) and at various target sizes.
• Participate in team meetings, conduct code reviews, and help uphold engineering standards across the team.
• Support and mentor junior engineers, sharing knowledge and good practice.
• Report software bugs and issues in the issue-tracking system in a timely manner.
• Develop and maintain documentation.

Your experience and skills 

• Tertiary qualification (Bachelor's or Master's) in computer science, engineering, mathematics or a related discipline.
• 2-4 years of demonstrated machine learning experience through professional work, research, or substantial personal projects.
• Confident working in Python, with solid hands-on experience in PyTorch.
• Strong understanding of the fundamentals of neural networks and convolutional neural networks (CNNs).
• Hands-on experience with object detection and/or segmentation problems.
• Familiarity with common libraries such as scikit-learn, scikit-image, PIL, Pandas and OpenCV.
• Sound research practices — able to read, understand and apply findings from technical literature.
• Eligibility to obtain and maintain an Australian Defence security clearance.

Desirable: 

• Exposure to C++.
• Experience with data science workflows and data pipelines.
• Technical experience reading from and controlling cameras.
• Experience deploying and optimising models on edge devices or embedded hardware.
• Exposure to airborne remote sensing, GIS or optical technologies.
• Strong physics and mathematics background.
• Examples of personal or open-source technical projects (GitHub links welcome).