Software Engineer, Machine Learning Infrastructure

Welcome to Planet. We believe in using space to help life on Earth.

Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one.

Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world’s toughest obstacles.

As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains.

We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world.

Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, and The Netherlands.

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
• 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

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

Compensation:

The US base salary range for this full-time position at the commencement of employment is $110,000 - $180,000.  Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location.  The range displays our typical hiring range for new hire salaries in US locations only.  Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

#LI-REMOTE

Why we care so much about Belonging. 

We’re dedicated to helping the whole Planet, and to do that we must strive to represent all of it within each of our offices and on all of our teams. That’s why Planet is guided by an