Machine Learning Engineer | Deep-Tech | Aerospace

Machine Learning Engineer | Automotive Aerospace | Deep-Tech

Location: Brisbane & Sunshine Coast based, hybrid

This is a unique opportunity for an experienced Machine Learning Engineer to join a fast growing, innovative start-up in the deep-tech and Automotive Aerospace sector.

As a Machine Learning Engineer you will be driving innovations in the latest technologies, building models for automotive aerospace systems. This includes vision (SLAM) and decision making models.

Experience and Qualifications:

• Minimum 2+ years of experience in machine learning, artificial intelligence, or related fields, with a specific focus on automotive aerospace applications.
• Expertise with machine learning frameworks such as PyTorch, TensorFlow, or similar, including hands-on experience with model development, training, and evaluation.
• Strong background in developing algorithms for supervised, unsupervised, and reinforcement learning models, alongside techniques like neural networks, decision trees, and more.
• Proficiency in programming languages including Python, C++, or Rust, with an in-depth understanding of data structures, algorithms, and performance optimization.
• Experience in data manipulation and analysis using tools such as NumPy, Pandas, and Scikit-learn.
• Proven experience in deploying machine learning models to real-world production systems or integrated environments.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Robotics, Automotive Engineering, Aerospace Engineering, or a related field.

Roles and Responsibilities:

• Develop, design, and implement cutting-edge machine learning algorithms tailored to the automotive aerospace sector, including systems like vision, SLAM (Simultaneous Localization and Mapping), and decision-making models.
• Collaborate with cross-disciplinary teams to determine data needs, collect training data, and create innovative solutions to enhance system performance.
• Build, train, and evaluate machine learning models to improve accuracy, efficiency, and reliability in automotive aerospace applications.
• Deploy and maintain machine learning models in real-time production environments, ensuring high-performance results.
• Continuously monitor and refine deployed models, retraining them to improve effectiveness and adapt to evolving requirements.
• Optimize existing algorithms for speed, scalability, and efficiency in complex automotive aerospace systems.
• Document machine learning processes, model architectures, and performance metrics for future reference and transparency.

This is the chance to join an innovative, fast growing technology company that are in the tech 4 good space.

If you are interested in finding out more please apply for immediate consideration.