Senior Software Engineer, Scientific Computing

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, implement, and sustain essential scientific computing libraries that will support KoBold’s mineral exploration analyses.

• Develop tools to enhance the speed of our machine learning advancements, which includes facilitating quick prototyping in Jupyter notebooks; creating frameworks for experimentation, evaluation, and simulation; transforming successful research and development into sturdy, scalable machine learning pipelines; and organizing models and their outputs to ensure repeatability and discoverability.

• Collaborate with data scientists to construct models that provide statistically valid predictions regarding the locations of economically significant concentrations of ore metals within the Earth's crust.

• Implement and mentor team members on engineering best practices, such as crafting robust, testable, and composable code.

• Partner with data scientists, geoscientists, and engineers to create a contemporary scientific computing stack for mineral exploration.

• Occasionally travel to exploration sites globally to assess the influence of scientific computing on KoBold’s exploration products and design new technologies to enhance discovery. Travel occurs approximately twice a year based on project requirements.

⛳️ Requirements

• A minimum of 5 years of experience as a software engineer, data scientist, or ML engineer, although most exceptional candidates will have close to 10 years.

• Proven history of developing production-quality data processing solutions or tools that have provided business value.

• Strong understanding of foundational machine learning concepts, including statistical, traditional, and deep-learning methodologies.

• Proficient in Python, preferably with experience in array-based libraries such as xarray and numpy.

• Extensive experience handling measured scientific data.

• Skilled in visualizing scientific data for subject matter experts.

• Familiarity with MLops and the development of reliable machine learning systems.

• Motivation to enhance the speed and efficacy of our data scientists in both experimental and production environments.

• Ability to deeply explore novel and challenging problems in applying machine learning to mineral exploration, including gaining insights into the complex domain of geology and mineral exploration practices while working with limited, disparate, and noisy data sources.

• A collaborative mindset to engage with stakeholders from diverse backgrounds (data scientists, geoscientists, software engineers, and operations).

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Flexible work environment and schedule.