Machine Learning Engineer, Forest Ecosystems

About the Role:

We are looking for a talented software engineer to join our Forest Ecosystems team. We are a cross functional team, with a highly collaborative culture, distributed remotely across the USA and Canada. In this role, you’ll work at the  intersection of machine learning (ML), software engineering, and remote sensing to develop, optimize, deploy, and maintain ML models that scale.

You’ll collaborate closely with engineers and data scientists to improve algorithms, integrate models into our distributed computing platform, and optimize and execute data pipelines. You’ll help maintain production ML models, support model performance monitoring, and design new workflows that enhance efficiency and reliability.

The Forest Ecosystems team 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 cutting-edge model architectures. 

This is a full-time position based remotely in the United States or Canada.

Impact You'll Own:

• Establish and maintain machine learning operations workflows for regular data generation
• Run experiments  to evaluate machine learning algorithms 
• 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

What You Bring:

• 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

What Makes You Stand Out: 

• 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

#LI-REMOTE

Application Deadline:

June 10, 2025 by 11:59pm PDT

Benefits While Working at Planet:

These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.

• 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

Compensation:

The US base salary range for this full-time position at the commencement of employment is listed below.  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.