Software Engineer, Planet

• Bachelor's or Master's degree in Computer Science or a related field
• Solid understanding of fundamentals in statistics and deep learning
• 4+ years of professional experience in software engineering, with a focus on machine learning infrastructure
• Proficiency with Python
• Proficiency with software engineering best practices such as version control, testing and continuous integration/continuous deployment (CI/CD)
• Experience with containerization and container orchestration tools like Docker, Kubernetes, and KubeFlow
• Experience with accelerated and distributed model training
• Experience implementing model versioning, monitoring and observability systems
• Excellent technical communication and documentation skills

Desirable

• You will be part of the Forest Ecosystems engineering team, which is on a mission to map, measure, and monitor the world’s forests using high resolution satellite imagery
• You will help us design, build, and scale our model training and evaluation infrastructure, and deploy model inference on imagery across the world
• Design and build scalable and reliable infrastructure to support model training efforts
• Design data schemas and storage patterns for geospatial training data, leveraging open source technologies where possible
• Optimize model training and inference performance for large datasets
• Develop and implement automated testing and monitoring frameworks to ensure the reliability of deployed models