Senior Machine Learning Engineer

About the Role:

Planet’s Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics for customers requiring robust change detection, object detection, and generative AI capabilities. As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense and Intelligence applications. You’ll implement novel embeddings-based change detection and advanced computer vision techniques. In this role, you will ensure best-in-class testing and deploy solutions to run at continental and global scales. You’ll collaborate closely with data scientists and software engineers to drive innovation in remote sensing and large-scale geospatial analytics. Ideal candidates bring a creative mindset and passion for solving complex geospatial challenges.

This is a full-time, hybrid role which will require you to work from our Arlington, VA office 3 days per week.

Impact You'll Own:

• Spearhead the development of novel algorithms and machine learning models tailored for Defense and Intelligence applications.
• Optimize model performance to execute high-throughput inference at continental and global scales.
• Innovate computer vision, time series, and embeddings-based techniques to uncover new insights from satellite data.
• Collaborate with product managers, data scientists, and engineers to define requirements and iterate on algorithm designs.
• Integrate ML pre-processing and inference pipelines seamlessly with adjacent software engineering platforms.
• Establish best-in-class testing, validation, and monitoring frameworks for continuous model reliability.

What You Bring:

• 10+ years of relevant experience of which 6+ years of experience is in machine learning.
• Ability to conduct a rigorous evaluation of results and internal communication of algorithm failure modes.
• Expertise with data science, time series methods, computer vision, and embeddings.
• Ability to implement, train, and optimize neural networks.
• 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.
• Experience writing clean, modular Python code and applying software development best practices (Git, testing, CI/CD).
• Experience deploying models (via Docker, Kubernetes, or similar) with an understanding of best practices for monitoring and maintaining them at scale.
• AWS or GCP experience
• Excellent communication skills, capable of explaining technical topics to diverse audiences.
• Graduate degree in a STEM or analytics-focused field or equivalent work experience.
• Located in the Washington, DC metro or ability to work and commute to Arlington, VA 3x/week
• Ability to obtain and maintain US Security Clearance

What Makes You Stand Out:

• Practical knowledge of remote sensing, satellite imagery, or related geospatial domains
• Knowledge of coordinate reference systems, geometry manipulations, and common data formats (GeoTIFF, GeoJSON, etc).
• Hands-on experience building geospatial or sensor-driven data products from scratch
• Familiarity with techniques like model compression, GPU optimizations, or distributed training pipelines

EAR/ITAR Requirements:

This position requires access to export-controlled information, and as such, employment (or hiring of a contractor) is contingent upon the candidate’s ability to access all applicable export-controlled information without additional export licensing being required by the Bureau of Industry and Security and/or the Directorate of Defense Trade Controls.

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
• 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