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Earth AI
About the role
EARTH AI is a fast-growing tech startup transforming the way critical metals are discovered. Founded in 2016 in Sydney, we are on a mission to secure the future of technology and infrastructure by responsibly sourcing the metals essential for global advancement. Using cutting-edge technology, we identify and develop new metal deposits with unmatched efficiency and sustainability. Our vision is to lead the industry by discovering and owning the majority of future metal deposits while becoming the most advanced, efficient, and responsible metal commodity company in the world.
We are seeking a passionate and driven Senior AI Researcher to join our team. This is an exciting opportunity to contribute to the development and deployment of cutting-edge AI solutions that support Earth AI’s mission to create meaningful impact in environmental applications. In this role, you will collaborate with our multidisciplinary team to design, implement, and refine data-driven models and analytics pipelines that power our AI systems, ensuring they are robust, efficient, and ready to tackle real-world environmental challenges.
Key Responsibilities:
Lead research initiatives in areas such as deep learning, reinforcement learning, generative models, or applied foundation models
Design and implement novel algorithms that translate into high-impact real-world applications
Analyze large datasets to uncover trends, anomalies, and opportunities
Contribute to the development of internal tools and reusable codebases
Collaborate with geoscientists, drillers and operations teams
About You:
Degree in Computer Science, Machine Learning, Mathematics, or a related field.
5+ years of experience in applied AI/ML research (industry or academia)
Deep expertise in one or more areas: generative models (e.g., diffusion, transformers), optimization, large language models, multimodal AI, etc.
Strong coding skills in Python and ML libraries (PyTorch, JAX, TensorFlow)
Track record of published research and/or shipped products
Bonus: experience with GPU clusters, model deployment pipelines, or AI safety/robustness
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