Founding Machine Learning Engineer
About the role
Mineflow is hiring a Founding Machine Learning Engineer to design and deploy ML models and data systems for mineral exploration. The role focuses on creating model architectures, training schemes, and data pipelines to handle large geological datasets, with emphasis on performance, latency, and scalable parsing of datasets for model consumption.
Responsibilities
- Design and implement model architectures and training schemes to maximize performance on massive geological datasets
- Work closely with the Mineflow team to further improve model training/inference accuracy and latency
- Build systems for automatically parsing datasets into model readable formats
Qualifications
- BS in Computer Science, Artificial Intelligence, Math or some other rigorous technical field
- Hands-on experience training and deploying machine learning models
- Familiarity with ML state of the art, recent publications
- Proactive and highly ambitious engineer who thrives in a fast-paced environment
Skills
About Mineflow
Mineflow predicts the shape and location of mineral deposits
Mineflow is an AI platform for mineral exploration. We generate predictions for mineral deposit shapes and locations, empowering mining companies from initial exploration to advanced feasibility studies. Geologists need to know precisely where their deposits are because drilling is expensive. In early trials with a lithium mining company, we found that Mineflow predicts the shape of hard rock lithium deposits more than an order of magnitude more accurately than our competitors. Ryan received a BS in Artificial Intelligence from Carnegie Mellon's School of Computer Science where he helped teach both the grad-level Deep Learning course and the grad-level Search Engines course. He built ML models at Google in the Display Ads optimization org for 2 years and launched products that generated ~125m USD in ARR.