JOIN US IN OUR MISSION TO SUPPORT A HEALTHIER PLANET.

CarbonLink is a pioneering company aiding Australian primary producers and landholders in their journey to sequester carbon and regenerate landscape. Regenerative agricultural practices have tremendous potential to combat climate change by drawing down and removing large quantities of atmospheric carbon dioxide into the soil, in addition to increased productivity of farmland, draught resilience, soil health and biodiversity.
CarbonLink has generated over 90% of Australia’s soil carbon credits. We bring over a decade of industry-leading experience, ground-breaking technology, streamlined processes, and unparalleled client service to the table. We combine industry leading measurement and modelling for Monitoring, Reporting and Verification approaches with practical on-farm advice to maximise project outcomes. Our diverse team includes machine learning researchers, software engineers, scientists, GIS experts, agricultural and soil specialists, and carbon project advisors.

• Machine Learning Infrastructure Development: Design, implement, and manage scalable, efficient, and secure ML pipelines for training, deploying, and monitoring models in production.
• Cloud and DevOps Engineering: Develop cloud-based solutions for ML model deployment and integrate best practices in DevOps, CI/CD, and infrastructure as code.
• Data Engineering: Build and optimize data pipelines for ingesting, processing, and storing large-scale soil and carbon-related datasets.
• Model Deployment & Monitoring: Deploy ML models into production environments and establish robust monitoring systems to ensure reliability, scalability, and compliance.
• Collaboration with Cross-Functional Teams: Work closely with data scientists, software engineers, and domain experts to align ML infrastructure with business and scientific objectives.
• Security & Compliance: Ensure all ML operations adhere to industry best practices for security, privacy, and regulatory compliance.
• Performance Optimization: Continuously improve ML workflows and infrastructure for efficiency, cost-effectiveness, and performance.
• Software Engineer: Contribute to the migration of legacy code to a modern software stac