Senior Software Engineer, Planet

• 6+ years of relevant experience of which 5+ years of experience is in machine learning
• Bachelor’s degree in Computer Science or similar
• Deep familiarity with time series methods, computer vision, and embeddings; able to implement, train, and optimize neural networks
• Data handling & preprocessing: Experience wrangling large datasets, ideally with geospatial libraries, combined with frameworks like PyTorch/TF for model development and training
• Ability to experiment with model architectures, and derive data-driven insights to iteratively improve performance and accuracy using an analytical mindset
• ML engineering experience: Comfortable writing clean, modular Python code and applying software development best practices (Git, testing, CI/CD)
• Hands-on production expertise: Experience deploying models (via Docker, Kubernetes, or similar) and understand best practices for monitoring and maintaining them at scale
• AWS or GCP experience
• Excellent communication skills, capable of explaining technical topics to diverse audiences
• If this job intrigues you, but you’re thinking you might not have all the qualifications, please... do apply!

Desirable

• Planet’s Built Environment applied machine learning team delivers advanced geospatial products primarily for external customers, with a focus on advanced analytics such as change detection, object detection, and emerging generative AI capabilities
• This role is a blend of hands-on engineering and modeling: you’ll implement novel methods (e.g. , deep learning for time series and computer vision), ensure best-in-class testing and validation, and deploy solutions to run at continental and global scales
• You’ll collaborate with both data scientists and software engineers to drive innovation in remote sensing and large-scale geospatial analytics
• This is a full-time, remote position based in the United States
• If located near an office, you are expected to work from that office 3 days per week
• End-to-end model development & maintenance: Develop new algorithms or methods, implement and test them rigorously, and integrate them into production pipelines. Contribute to their ongoing maintenance and iteratively improve them
• Advancing geospatial analytics: Innovate on computer vision, time series, and other ML techniques to uncover new insights from satellite and aerial data
• Cross-functional collaboration: Partner with product managers, data scientists, and engineers to define requirements, validate model outputs, and refine algorithms in iterative cycles
• Collaborating with adjacent ML and software engineering teams to ensure seamless integration of ML pre-processing and inference steps, defining best practices for efficient deployment and maintenance of geospatial models

• Application Deadline: February 11th, 2026, 11:59pm PST