Senior Machine Learning Engineer

About the Role:

Planet's Built Environment applied machine learning team delivers advanced geospatial products primarily for a variety of customers who require robust analytics such as change detection, object detection, and emerging generative AI capabilities. This role is a blend of hands-on engineering and modeling focused on Defense and Intelligence applications. 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. The ideal candidate has a passion for innovation-you bring a creative mindset when solving complex problems.

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

Impact You'll Own:

• 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.

What You Bring:

• 6+ years of relevant experience of which 5+ years of experience is in machine learning
• Deep familiarity with time series methods, computer vision, and embeddings; able 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.
• Bachelor's degree in a STEM or analytics-focused field or equivalent work experience.

What Makes You Stand Out:

• Ability to work and commute to Arlington, VA 3x/week
• Ability to obtain and maintain US Security Clearance
• 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
• Ability to complex problem-solve while working within the constraints of our compute environment.

Application Deadline:

September 29, 2025 by 11:59 PM 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
• 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.