Machine Learning Engineer at Seeing Machines

Canberra, Victoria, Australia -

Full Time

Start Date

Immediate

Expiry Date

18 Jun, 26

Salary

0.0

Posted On

20 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Physics-based Simulation, Computer Vision, Robotics, Synthetic Data Generation, Generative AI, Python, 3D Geometry, Camera Geometry, MLOps, Docker, MLFlow, MetaFlow, Scenario Creation, Data Pipelines, Problem-Solving

Industry

IT Services and IT Consulting

Description

About the company: Seeing Machines has developed the world’s most advanced human data-driven technology which enhances transport safety by dramatically reducing fatal accidents every day. We’re on a mission to achieve zero transport fatalities. With at least 1 million cars on the road using our state-of-the-art operator monitoring technology developed by the passionate team at Seeing Machines, we provide real-time protection from distraction and drowsiness-related driving events. Seeing Machines works with many of the world’s leading brands (including General Motors, Mercedes Benz, Qantas, Caterpillar, Toll) across the transport sectors automotive, commercial road transport (Fleet), and aviation. Position Summary: We are seeking an outstanding applied Machine Learning Engineer with hands‑on experience using physics‑based simulation engines to build simulation-driven synthetic data pipelines for computer vision and robotics applications. This is a hands-on role, and you will be working closely with a team of multi-skilled machine learning researchers, engineers and technology partners from diverse backgrounds in exploring the frontiers of computer vision driven technology. Key Responsibilities: Exploring and leveraging off the state-of-the-art physics-based simulators and foundational tools to build and operate tools for scenario creation, simulation execution, and generation of annotated 3D and multi-modal synthetic datasets. Exploring and adopting generative AI synthetic data generation (SDG) techniques to bridge the sim-to-real gaps in synthetic datasets. Collaborating closely with researchers, engineers and technology partners to mature emerging technologies, supporting integration through proof‑of‑concepts or product pathways and enabling effective hand‑off to downstream teams. Contributing to MLOps and Data pipelines and engineering practices using tools like Docker, MLFlow, and MetaFlow. Most importantly, a strong drive to learn, experiment, adapt flexibly to changing contexts, and push the boundaries of what is technically possible! Skills/Experience and Qualifications Mandatory: ● Bachelor's qualification in Advanced Computing, Computer Vision, Machine Learning, Computer Science, or an equivalent field. ● Hands‑on experience with physics‑based simulation engines such as MuJoCo, NVIDIA Isaac Sim or equivalent. ● Good understanding of 3D geometry and Camera geometry (e.g. coordinate frames, transforms, and 3D–2D projection models). ● Applied Machine Learning skills, preferably with applications in computer vision and/or robotics. ● Strong programming skills and experience developing in Python. ● Excellent problem-solving abilities and strong verbal and written communication skills. ● A collaborative team player who can work effectively across multiple teams. ● A proactive mindset, with an eagerness to try new approaches and solve complex problems. Desirable: ● Experience building synthetic data generation pipelines for computer vision applications, e.g. in the fields of automotive or robotics. ● Experience applying generative AI augmentation with ground truth label integrity in simulation to bridge sim-to-real gaps. ● Experience with 3D Computer Vision & Point Cloud Processing. ● Experience with MLOps tools such as Docker and MLFlow. Experience with Data tools such as Metaflow, Streamlit or equivalent.

Responsibilities

The role involves exploring and leveraging physics-based simulators to build tools for scenario creation and generating annotated 3D and multi-modal synthetic datasets. Responsibilities also include adopting generative AI techniques to bridge sim-to-real gaps and contributing to MLOps and data engineering practices.