Job Url: https://boards.greenhouse.io/embed/job_app?token=7374537&utm_source=jobright&jr_id=691cded922a2cb4b9bd1e0df

Job Description: Senior Software Engineer, Machine Learning
at Planet (View all jobs)
United States, Remote
Welcome to Planet. We believe in using space to help life on Earth.

Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one.

Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world’s toughest obstacles.

As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains.

We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world.

Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, Austria, Slovenia, and The Netherlands.

About the Role:

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.

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
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
What Makes You Stand Out: 

Bachelor’s degree in a STEM or analytics-focused field 
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