Avalanche is a Seattle-based start-up developing compact fusion machines for clean, energy-dense power generation at a scale you can hold in your hands.  Our unique approach allows us to build, test, break, learn and iterate in days.  The modular design is designed to provide power to applications from austere environments to deep sea vehicles and maritime transport to lunar power. 

We know we have an incredibly ambitious mission, and that it requires a wide combination of skills, attitude, and grit to make it happen. If you have a passion for abundant energy, and how we can improve life with it, we want to have a chat with you.

Avalanche might be fit for you if: You are self-motivated, energized by problem solving and working closely with hardware. You enjoy hands-on, creative collaboration with a small, tight-knit team you can work with, laugh with, and learn with on a wide variety of topics.  You can learn and pivot between priorities when necessary and you can slide the headphones on, focus, and auger deep into a single topic as needed.  You thrive in an atmosphere of development, and you are an ardent learner who’s always looking for ways to grow and improve.

About the Role

A Research Software Engineer is responsible for developing and maintaining software and data infrastructure for scientific data collection, processing, and communication. They lead the formation of various code-based packages for standardized scientific analysis under the direction of science and engineering teams.

In this role you’re responsible for:

• Developing and maintaining standards for common scientific data-processing and analysis toolboxes under scientific guidance
• Developing and maintaining common scientific data-processing and analysis toolboxes under scientific guidance
• Translating written or verbal analysis methods into reviewed, maintainable, tested, and reusable Python modules
• Working with diagnostic experts to develop and maintain software for processing data
• Establishing and maintaining standards for code review, testing, documentation, packaging, and release management
• Reviewing analysis code for correctness, maintainability, and consistency
• Incorporating feedback from scientists and engineers into shared analysis tools
• Collaboratively developing clear and effective scientific data visualizations
• Supporting the transition of exploratory scripts into standardized analysis software