The New Standard in Mineral Intelligence

Where most people see a rock, we see data. Hypermine is a deep-tech startup engineering breakthrough hyperspectral laser to see beyond the surface: delivering real-time mineralogical data right in the mines, which was previously impossible to capture outside of a laboratory. Our technology gives real-time mineral insights to help maximize efficiency and minimize the environmental impact in mining, by allowing our customers to optimize their operations, reduce waste, and transition toward more sustainable extraction processes. 

What we are doing is not just building another sensor; we’re building the foundational intelligence for sustainable mining. We develop and manufacture our high-performance laser-based sensing equipment entirely in-house, leveraging our proprietary, patented technology to deliver precise and reliable solutions. We are seeking an inspired professional to join our international, fast-paced team solving a real-world problem on a massive scale. If you want to be part of creating world-first technology for the harshest environments on Earth, we should talk! 

The Role 

We are looking for an Applied Software Engineer to join our team in Espoo. This is a full-time position that might include occasional business travel. 

The Applied Software Engineer will be responsible for: 

• Designing and implementing performant, safe, and well-tested systems-level Rust software 
• Developing and applying chemometric and machine learning methods to analyze hyperspectral data 
• Maintaining, extending, and optimizing a large Rust codebase responsible for hardware interfacing and control 
• Building and validating models for mineral identification, sample mineralogy, and lithological classification from spectral signatures 
• Debugging and troubleshooting complex issues across hardware-software boundaries 
• Interpreting and communicating analytical results to technical and non-technical stakeholders 

Required Qualifications 

• 3 years of proficiency in at least one compiled systems language; experience with Rust is preferred, or a demonstrated willingness and ability to learn it quickly 
• Proficiency in the Python data science ecosystem (NumPy, scikit-learn, spectral libraries) 
• Experience working with large, production-grade codebases 
• Solid foundation in data science, statistical modeling, and/or machine learning in real-world industrial settings 
• Demonstrated experience working with hyperspectral or multispectral data 
• Familiarity with spectroscopy principles and spectral reference libraries 
• Degree (Bachelor's or Master's) in Computer Science, Geoscience, Physics, or a comparable field 

Preferred Qualifications 

• Familiarity with hardware communication protocols (serial, SPI, I2C, etc.) 
• Background in geological engineering or a related geoscience field 
• Knowledge in optical sensing systems, preferably hyperspectral 
• Understanding of mineralogy and lithology concepts sufficient to inform model development and validate results 

What We Offer 

• Meaningful Innovation: Work on a breakthrough "world-first" product where your contributions aren’t just incremental but foundational to our technology’s success. You will be part of building a technology that is a key enabler for the efficient extraction of critical minerals required for the global energy transition. 
• Real-World Application: You will play a pivotal role in developing first-of-its-kind technology. You will be able to actively shape our innovation and see your work deployed in some of the most challenging environments on the planet. 
• A Culture of High Agency: Engage in high-level problem-solving, working with a multidisciplinary team of experts. We value high-agency individuals who want to make an impact. 
• Valued Core Team Member: As an early hire, you will be a cornerstone of the Hypermine story. We offer competitive compensation that reflects the high value we place on our early-stage talent. 

We will review the applications and begin interviews as they come in. We encourage early submissions, as the position will be filled as soon as the ideal candidate is found.