Senior Software Engineer, Machine Learning

Summary

Join Planet's Built Environment applied machine learning team and contribute to the development and maintenance of advanced geospatial products using cutting-edge machine learning techniques. You will be responsible for end-to-end model development, implementing novel methods, ensuring rigorous testing and validation, and deploying solutions at scale. This role involves collaborating with data scientists and software engineers to drive innovation in remote sensing and large-scale geospatial analytics. The position is full-time and remote, based in the United States. You will be advancing geospatial analytics through innovation in computer vision, time series, and other ML techniques. Planet offers a comprehensive benefits package and a remote-friendly work environment.

Requirements

• 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
• Data handling & preprocessing: Experience wrangling large datasets, ideally with geospatial libraries, combined with frameworks like PyTorch/TF for model development and training
• Analytical mindset: Able to experiment with model architectures, and derive data-driven insights to iteratively improve performance and accuracy
• Proven ML engineering experience: Comfortable writing clean, modular Python code and applying software development best practices (Git, testing, CI/CD)
• Hands-on production expertise: You’ve deployed 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
• Master’s degree in a STEM or analytics-focused field or equivalent work experience

Responsibilities

• 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
• Innovate on computer vision, time series, and other ML techniques to uncover new insights from satellite and aerial data
• 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

Preferred Qualifications

• Practical knowledge of remote sensing, satellite imagery, or related geospatial domains
• Experience implementing advanced time series approaches, including state-of-the-art deep learning architectures (e.g., Transformers, RNN variants) or novel forecasting methodologies that can perform online inference
• 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
• Performance optimization: Familiarity with techniques like model compression, GPU optimizations, or distributed training pipelines
• Passion for innovation: you bring a creative mindset and a capability for solving complex problems while working within the constraints of our compute environment

Benefits

• 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