ABOUT US

At Cornerstone Solutions, we’re pioneering next-generation industry intelligence platforms that merge advanced software engineering with applied AI. Based in Melbourne, we design and build complex analytics systems that transform industry operations, enabling smarter decision-making, risk reduction, and sustainable growth.

REQUIRED QUALIFICATIONS

• Bachelor’s degree (Master’s/PhD preferred) in Computer Science, Software Engineering, Machine Learning, Applied Mathematics, or related fields.
• 5+ years of experience in software design, development, and deployment of advanced ML/AI systems.
• Expertise in Python, software engineering practices, and ML frameworks (PyTorch, TensorFlow, Hugging Face, Scikit-learn).
• Strong background in software lifecycle management — requirements analysis, coding, testing, debugging, documentation, and maintenance.
• Demonstrated experience building and optimising cloud-native and distributed ML applications (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Advanced understanding of computer vision, NLP, or large-scale predictive analytics.

PREFERRED QUALIFICATIONS

• Experience integrating AI into geospatial, drone, or site camera systems.
• Contributions to research publications, patents, or open-source engineering projects.
• Strong knowledge of algorithm optimisation, high-performance computing, and GPU acceleration.

ROLE OVERVIEW

We are seeking a Lead Software Engineer (Machine Learning & Applied AI) with advanced expertise in designing, developing, testing, and maintaining high-performance software systems powered by artificial intelligence.
This is a specialized role that requires both in-depth knowledge of machine learning and extensive experience in software engineering. You will architect and productionise advanced algorithms, optimise large-scale data processing systems, and integrate complex AI models into mission-critical analytics platforms.

KEY RESPONSIBILITIES

Software Architecture & Development

• Design, develop, test, and maintain advanced software applications and AI-driven analytics systems.
• Architect scalable, modular, and distributed ML/AI systems for real-time industry data.
• Write and review production-grade code, technical specifications, and system documentation.

Applied AI & Research Translation

• Implement predictive analytics, computer vision, NLP, and generative AI models for industrial use.
• Translate state-of-the-art academic research into efficient, maintainable production software.
• Apply advanced optimisation, mathematical modelling, and algorithmic techniques.

Systems Engineering & MLOps

• Build automated pipelines for data ingestion, model training, deployment, monitoring, and retraining.
• Ensure software reliability, version control, automated testing, and continuous integration.
• Implement monitoring, diagnostics, and fault-tolerant recovery in large-scale ML systems.

Technical Leadership & Governance

• Provide technical direction and mentorship to engineers and data scientists.
• Establish coding standards, quality assurance practices, and system architecture reviews.
• Consult with internal stakeholders to evaluate requirements, limitations, and system performance.