Intern, Edge Compute

Currently pursuing or recently completed a degree in Computer Science, Robotics, Computer Engineering, Aerospace Engineering, Electrical Engineering, or a related field.

A solid understanding of deep learning fundamentals, particularly in Computer Vision (CNNs or Vision Transformers).

Proficiency in Python and hands-on experience with at least one major ML framework (e.g., PyTorch, JAX).

The ability to break down complex problems and a strong desire to learn how to deploy models on resource-constrained hardware.

Excellent communication skills with the ability to document technical workflows and explain model trade-offs (e.g., accuracy vs. speed vs power).

A collaborative mindset and the ability to work effectively within a cross-functional team of engineers.

These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.

Commuter Benefits

Paid time off for holidays and company-wide days off

Internet reimbursement

Access to LinkedIn Learning

Planet's mission is to image the entire world every day, making global change visible, accessible, and actionable. We've successfully captured daily imagery of the Earth, and now we're taking the next bold step: making our spacecraft smarter and more efficient using AI and machine learning.

We are seeking a talented AI/ML Intern with an emphasis on geospatial analytics and remote sensing to join our Edge Compute team.  This is a high-impact role for an ambitious undergraduate or graduate student to work at the intersection of orbital mechanics, computer vision, and hardware-constrained systems. You will help build the algorithms of our next-generation satellites, moving beyond simple image capture to autonomous vision systems that can detect events and react to dynamic Earth conditions in real-time. The ideal candidate is passionate about AI/ML, autonomy, and squeezing performance out of neural networks to run them in the harsh, resource-constrained environment of space.

This is a full-time, hybrid role which will require you to be in our San Francisco, HQ 3 days per week.