Software Engineer

We are looking for a startup ready ML & computer vision expert for Fire AI, the next generation of wildfire fighting technology. You’ll be joining an existing team of developers that include full stack developers and devops to build out a new ML pipeline. You’ll be responsible for setting up the training for a new model based on our existing dataset and creating a runtime that can be used both by our teams in the field without an internet connection and in the cloud. In addition, we need a generalist that is open to trying new ideas, expanding their skillset, and building on our culture of learning - in addition to your primary responsibility of building and maintaining the ML, you’ll also help out building the backend and infrastructure needed to use it. Building a new product is a highly collaborative effort, and as such, a proactive, strong team player is going to thrive in our work environment.
Responsibilities

• Train and develop a new ML model, including making decisions around annotation, training, and runtime
• Build backend APIs and libraries to help connect the ML to the rest of the application
• Analyze failure messages, write tests, and build a strong ecosystem of quality for the backend
• Work closely with the frontend, fullstack, and ops people in our team to build amazing experiences, front to back

Experience and skills that are required:

• Experience with image processing and machine learning
• Experience with 3D mathematics, including camera modeling and raycasting
• Experience with AWS, Azure, Digital Ocean, GCP, or comparable platforms
• Proficiency with writing automated tests, including unit, integration, and end-to-end
• Proficient with Git
• Objective oriented self starter attitude

Bonus points for having:

• Experience with GIS
• Experience with AWS, including
• DynamoDB
• Step Functions
• A passion for performance and understanding how to create a performant apps
• A passion for pulling things open, figuring out how they work, and putting them back together better
• Experience using post-mortem analysis, such as log files or metrics, to diagnose and debug issues