Machine Learning Engineer

Summary

Join Planet's Forest Ecosystems team as a talented software engineer and contribute to mapping, measuring, and monitoring the world's forests using high-resolution satellite imagery. You will work at the intersection of machine learning, software engineering, and remote sensing. This role involves developing, optimizing, deploying, and maintaining scalable ML models. Collaborate with engineers and data scientists to improve algorithms, integrate models into our distributed computing platform, and optimize data pipelines. This full-time, remote position is based in the United States or Canada. You will help maintain production ML models, support model performance monitoring, and design new workflows to enhance efficiency and reliability.

Requirements

• Bachelor's or Master's degree in Computer Science or a related field
• 4+ years of professional experience in software engineering of which 2+ years of this is experience in developing and designing Computer Vision and/or Machine Learning technologies and systems
• Proficiency with Python and machine learning frameworks like 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, Flyte or Temporal
• Experience implementing model versioning, monitoring and observability systems
• Excellent technical communication and documentation skills

Responsibilities

• Establish and maintain machine learning operations workflows for regular data generation
• Run experiments to evaluate machine learning algorithms
• Perform ML operations to maintain production algorithms (monitoring, training, benchmarking, deploying, etc)
• Develop and implement automated testing to ensure the reliability of deployed models
• Contribute to full-stack development, from backend and APIs to DevOps tasks and occasional front-end work

Preferred Qualifications

• Experience in remote sensing and geospatial data, particularly raster and LiDAR data
• Fluency with geospatial technologies in Python (e.g. GDAL, rasterio, shapely, STAC, xarray, etc)
• Experience with deep learning at scale in a geospatial and/or remote sensing context
• Demonstrated experience in managing large MLOps production workflows

Benefits

• Comprehensive Medical, Dental, and Vision plans
• Health Savings Account (HSA) with a company contribution
• Generous Paid Time Off in addition to holidays and company-wide days off
• 16 Weeks of Paid Parental Leave
• Remote-friendly work environment
• Wellness Program and Employee Assistance Program (EAP)
• Home Office Reimbursement
• Monthly Phone and Internet Reimbursement
• Tuition Reimbursement and access to LinkedIn Learning
• Equity
• Commuter Benefits (if local to an office)
• Volunteering Paid Time Off