Software Engineer, Machine Learning Infrastructure

About the Role:  

We are looking for talented software engineers to help us build our next-generation infrastructure to train, evaluate, and deploy ML models at scale. 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. We convert satellite imagery into quantifiable metrics like tree height and aboveground carbon using spatially-explicit deep learning models. We are continuously improving our models by expanding and curating our datasets, experimenting with different data sources (including optical, SAR, and LiDAR), and experimenting with bleeding-edge model architectures. You will help us design, build, and scale our model training and evaluation infrastructure, and deploy model inference on imagery across the world. We are a small and growing team, with a highly collaborative culture, distributed remotely across USA and Canada.

Impact You’ll Own:

• 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

What You Bring:

• 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
• 4+ years of experience writing code with Python
• Experience with TensorFlow or PyTorch
• 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

What Makes You Stand Out:

• Experience in remote sensing data, particularly optical and LiDAR data
• Fluency with geospatial technologies in Python (e.g. GDAL, rasterio, shapely, etc)

Benefits While Working at Planet:

• Comprehensive Health Plan
• Wellness program and onsite massages in specific offices
• Flexible Time Off
• Recognition Programs
• Commuter Benefits
• Learning and Tuition Reimbursement
• Parental Leave
• Offsites and Happy Hours
• Volunteering Benefits

#LI-REMOTE