Machine Learning Engineer

Summary

Join Planet's Forest Ecosystems team as a Software Engineer and contribute to mapping, measuring, and monitoring the world's forests using high-resolution satellite imagery and machine learning. You will work remotely in the US or Canada, collaborating with engineers and data scientists to develop, optimize, deploy, and maintain scalable ML models. Responsibilities include establishing MLOps workflows, running experiments, maintaining production algorithms, developing automated testing, and contributing to full-stack development. This full-time position requires a Bachelor's or Master's degree in Computer Science or a related field, 4+ years of software engineering experience (including 2+ years in developing and designing Computer Vision/ML technologies), and proficiency in Python and ML frameworks. Planet offers a remote-friendly work environment and various benefits including extended health and dental coverage, RRSP with company contribution, paid time off, and more.

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

• Extended Health and Dental Coverage
• Health Spending Account
• RRSP with company contribution
• Paid time off including vacation, holidays and company-wide days off
• Remote-friendly work environment
• Employee Wellness Program
• Home Office Reimbursement
• Monthly Phone and Internet Reimbursement
• Tuition Reimbursement and access to LinkedIn Learning
• Quality of Life Stipend
• Equity
• Volunteering Paid Time Off